Data science lead with 8 years of experience in recommendations, ranking and ads, currently leading a pod of 4 data scientists on personalization for a streaming service with 18M subscribers. Shipped a homepage ranking model that raised viewing hours 7%. Sets technical direction, reviews modeling work and stays hands-on in Python, PyTorch and experiment design.
Experience
Data Science Lead, Personalization, Marquee Streaming (Los Angeles, CA)
Jan 2023 – Present
Lead 4 data scientists building recommendation models for 18M subscribers
Shipped a two-tower retrieval and ranking model for the homepage, increasing viewing hours per member 7%
Defined the team's offline-to-online evaluation standard, raising launch success rate from 40% to 65%
Partner with product and engineering leads on a quarterly personalization roadmap
Senior Data Scientist, Soundpost Audio (Santa Monica, CA)
Feb 2020 – Dec 2022
Built playlist-continuation models that increased session length 11%
Designed a contextual bandit for podcast promotions, lifting follows 15%
Data Scientist, Clearview Ad Analytics (San Diego, CA)
Jun 2018 – Jan 2020
Built click-through prediction models for 300M daily impressions
Education
M.S. in Statistics, University of California, Los Angeles (Sep 2016 – Jun 2018)
B.S. in Applied Mathematics, University of California, Davis (Sep 2012 – Jun 2016)
A strong Data Science Lead resume shows the questions you answered, the models or pipelines you shipped, and the decisions your analysis changed. Lead with scope and impact: the size of the systems, budgets or teams you influenced, and the decisions you owned.
Sections, in order
Summary
Experience
Projects
Skills
Education
Length: One page is ideal; two pages are fine with 10+ years of relevant experience.
Summary: Open with your specialty and years of experience, then one or two signature results.
Skills to feature
SQL
Python or R
Statistics
Machine learning
Data modeling
dbt and ELT pipelines
BI tools (Looker, Tableau, Power BI)
Experiment design
Data visualization
Stakeholder communication
Example bullet points
Built a churn model (gradient boosting, AUC 0.86) that targeted retention offers and saved $900k a year
Rebuilt the revenue dashboard on dbt and Looker, cutting refresh time from 6 hours to 20 minutes
Designed 25 A/B tests with sequential analysis, shortening the average test from 4 weeks to 10 days
Tips from recruiters
Pair every technique with the decision it informed; "built a model" alone says little.
List tools under grouped skills ("Analytics: SQL, dbt, Looker") so ATS keyword matches are exact.
Link a notebook, Kaggle profile or public analysis if you have one.
Recommended templates
Classic (ATS-friendly): Jake's-style LaTeX classic: small caps and hairline rules
Modern (ATS-friendly): Clean single column with accent headings and rules
Compact (ATS-friendly): Dense single column that fits long histories
Data Science Lead resume FAQ
What should a Data Science Lead resume include?
A Data Science Lead resume should show the questions you answered, the models or pipelines you shipped, and the decisions your analysis changed. Use these sections in this order: Summary, Experience, Projects, Skills and Education. Lead with scope and impact: the size of the systems, budgets or teams you influenced, and the decisions you owned.
What is the best resume template for a Data Science Lead?
Classic, Modern and Compact work well for Data Science Lead roles. They are single-column with standard headings, so applicant tracking systems (ATS) parse them reliably.
How long should a Data Science Lead resume be?
One page is ideal; two pages are fine with 10+ years of relevant experience.
Which skills should a Data Science Lead put on a resume?
Prioritize the skills named in the job posting. Common ones for Data Science Lead roles are SQL, Python or R, Statistics, Machine learning, Data modeling and dbt and ELT pipelines.
How do I write strong Data Science Lead resume bullet points?
Start with an action verb, say what you did, and end with a measurable result such as forecast accuracy, revenue impact and query time. For example: "Built a churn model (gradient boosting, AUC 0.86) that targeted retention offers and saved $900k a year."