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Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI

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Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Classic

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Modern

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Minimal

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop. Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic. Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Creative

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop. Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic. Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Timeline

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop. Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic. Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Card

README.md

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Tech geek

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Academic

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Compact

Extraordinary CV · Vol. I

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Newsprint

❯ whoami --long

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

# Lattice AI // Operations Data Specialist

2023.03 - Present

- Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.

- Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.

- Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.

- Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.

- Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.

# Harbor Commerce // Senior Data Analyst

2021.10 - 2023.03

- Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.

- Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.

- Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.

- Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.

# Northstar // Operations Data Analyst

2018.07 - 2021.10

- Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.

- Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.

- Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.

- Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.

Open resume preview
Use this
Terminal

Extraordinary CV · Vol. I

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Swiss grid

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Ink

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop. Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic. Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Dashboard

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Elegant
R

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
Open resume preview
Use this
Photo sidebar

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
Open resume preview
Use this
Consulting

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Formal

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop. Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic. Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
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Infographic

README.md

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Notion

README.md

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
GitHub

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
Open resume preview
Use this
Forest

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
Open resume preview
Use this
Burgundy

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Coral

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Banner

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Executive

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop. Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic. Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Bento

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Aurora

Extraordinary CV · Vol. I

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Editorial

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop. Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic. Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Color tabs

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
Open resume preview
Use this
Two-column

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Sharp

Extraordinary CV · Vol. I

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Catalog

Extraordinary CV · Vol. I

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Gazette

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
Open resume preview
Use this
Olive
R

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
Open resume preview
Use this
Sakura

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
Open resume preview
Use this
Amethyst

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop. Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic. Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
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Warm bento

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Summary

Riley Chen has nearly 7 years in internet and AI analytics, moving teams from ops-by-instinct to decisions they can defend. Builds metric systems and data foundations from scratch: 10M+ user behavior, funnel diagnostics, lifecycle management, and competitor signal. SQL, Python, machine learning, and BI cover the path from instrumentation to a closed strategy loop.

Growth results: data-led product and ops work has lifted conversion 18%+, high-activity share 8 points, and single-program incremental GMV or recovered revenue from tens of millions into nine figures. Peak campaign DAU landed 25% above organic.

Methods in production: gradient boosting, sequence mining, A/B tests, and attribution sit inside retention, monetization, and anomaly workflows. Minute-level alerts cut incident location from 25 minutes to 2 — a fast data base for growth and reliability.

Experience

Lattice AI

2023.03 – Present

Operations Data Specialist

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
Machine learningRetentionDashboardsCompetitive intel

Harbor Commerce

2021.10 – 2023.03

Senior Data Analyst

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
PythonFunnelsA/B testsTableau

Northstar

2018.07 – 2021.10

Operations Data Analyst

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
SQLCohortsActivity modelsExcel

Projects

Internal IM platform

2021.02 – 2021.12

Data architecture lead

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
InstrumentationSQLRealtime

Fulfillment platform

2022.01 – 2022.12

Analyst

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
PythonSupply chain

Growth model

2019.03 – 2019.12

Core member

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
LTVClustering

Competitor intelligence

2023.07 – 2024.06

Project lead

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
NLPCrawlersPower BI

Education

University of California, Berkeley

B.A. Statistics · 3.7/4.0

Inference, regression, and statistical computing.

2014.09 – 2018.07

Skills

SQL98%
Python93%
Growth analysis90%
Excel / Sheets80%
BI & visualization78%
Machine learning72%
Competitive analysis70%
Instrumentation55%
Supply chain52%

Awards

  • Best analytics team, company-wide — 2019.01
  • Employee of the year — 2022.01
  • AI applied-innovation award — 2024.01
Open resume preview
Use this
Sandstone

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
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Terracotta
R

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
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Use this
Plum

Riley Chen

Operations Data Analyst

+1 (415) 555-0187 · riley.chen@email.com · San Francisco, CA · linkedin.com/in/rileychen · github.com/rileychen · rileychen.data

Experience

2023

Operations Data Specialist

Lattice AI

2023.03 – Present

  • Designed the 0→1 metric system for a conversational product; made useful-conversation rate the north star, unified definitions across teams, and cut decision cycle time 40%.
  • Built a retention attribution model (gradient boosting + sequence mining) that flags the drop-off node and a next action. 30-day retention 51% → 62%.
  • Shipped an LTV and price-sensitivity dashboard; 12 subscription experiments lifted paid conversion 37% and added $2.1M ARR.
  • Added adaptive anomaly detection on activate → engage → pay. Payment-drop discovery fell from hours to 9 minutes; ~$900k recovered in a quarter.
  • Built a competitor radar from performance and feature-adoption data; three retention bets lifted weekly active users 28%.
#Machine learning#Retention#Dashboards#Competitive intel

2021

Senior Data Analyst

Harbor Commerce

2021.10 – 2023.03

  • Diagnosed a PDP-to-checkout leak. Path attribution in Python found slow load and missing stock alerts; conversion +18% and +$12M quarterly GMV.
  • Ran a 6-cell payment A/B on layout, default method, and offer placement. Success rate 68% → 79%; ~$8M abandoned-cart GMV recovered.
  • Built a near-line cross-sell engine from browse and cart sequences. Attach rate +2.5 pts, ARPU +11%, ~$15M annual incremental GMV.
  • Rebuilt promo monitoring in Tableau with minute-level alerts across warmup, surge, and encore. Caught 3 conversion cliffs in S-tier events and saved $3M+.
#Python#Funnels#A/B tests#Tableau

2018

Operations Data Analyst

Northstar

2018.07 – 2021.10

  • Stood up a 12-metric daily/weekly pack (DAU, retention, feature penetration). SQL + Sheets cut production from 1 day to 2 hours.
  • Segmented 10M+ behavior logs (activation, win-back, high-value). Linked churn thresholds to outreach; high-activity share +8 pts in 6 months.
  • Scored activity from entry, depth, and spend; a targeted recall saved 500k at-risk users and lifted next-month retention ~3 pts.
  • Forecast dashboards for two S-tier campaigns. Peak DAU landed 25% above organic with no core-service degradation.
#SQL#Cohorts#Activity models#Excel

Projects

2021

Data architecture lead

Internal IM platform

2021.02 – 2021.12

  • Designed 150+ standard events for a 100k-DAU internal messenger — message, A/V, and collab — with one parameter spec.
  • Shipped 10+ second-level cockpits. Mean time to locate an incident 25 min → 2 min; critical delivery 99.99%.
#Instrumentation#SQL#Realtime

2022

Analyst

Fulfillment platform

2022.01 – 2022.12

  • Python fulfillment funnel on pick / pack / ship. Isolated outbound delay and sort backlog; mean delivery 32h → 27.2h (−15%); same-day share cleared 67%.
#Python#Supply chain

2019

Core member

Growth model

2019.03 – 2019.12

  • LTV tiers plus K-Means/PCA on tens of millions of users. Activation cost −18%, sleep-to-wake conversion +23%, monthly retained actives +11%.
#LTV#Clustering

2023

Project lead

Competitor intelligence

2023.07 – 2024.06

  • Stood up a crawl + NLP stack across 10+ competitors (store, web, social). ~2M unstructured rows/day into a T+1 Power BI board.
  • Flagged an enterprise pivot early; three features shipped first and new-feature adoption beat the historical mean by 16 pts.
#NLP#Crawlers#Power BI
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Slate