Data scientist resume example
Data science resumes are evaluated by both automated ATS and technical reviewers who will probe your methods. Lead with impact — business outcomes, not just model metrics — and make your stack immediately visible.
Data scientist resume sample
Summary
Data scientist with 6 years building and deploying ML models in e-commerce and fintech. Known for translating model outputs into revenue decisions: last churn model saved an estimated $2.4M in annual retention spend. Comfortable owning the full pipeline from raw data to production API.
Experience
- Built a gradient-boosted churn propensity model (XGBoost, Python) deployed to 4M active customers; reduced 90-day churn by 18%, estimated $2.4M annual retention uplift.
- Designed A/B testing framework (statsmodels) now used by 6 product teams; cut decision cycle from 3 weeks to 8 days.
- Replaced a manual pricing rules engine with an ML-based dynamic pricing model; increased gross margin by 2.1 pp on the 200K SKU catalog.
- Mentored 2 junior data scientists; introduced feature store (Feast) reducing duplicate feature engineering by ~60%.
- Built credit-risk scorecard (logistic regression + SHAP explainability) approved for regulatory use; lifted loan approval rate 7% while keeping default rates flat.
- Reduced data pipeline runtime from 6h to 45min by moving batch ETL to Spark on AWS EMR.
Education & Credentials
MS Data Science, UT Austin (2020) · BS Statistics, UC Davis (2018)
Technical Skills
Python (pandas, scikit-learn, XGBoost, PyTorch) · SQL · Spark (PySpark) · AWS (S3, EMR, SageMaker) · dbt · Feast · Tableau · Git / MLflow
What to include on a data scientist resume
- Business impact over model accuracy — “AUC 0.91” means little to a hiring manager; “churn model saved $2.4M” means everything. Lead with the outcome.
- Full stack visibility — show you can take data from raw source to production endpoint: ETL/ELT, feature engineering, model training, deployment (API or batch), monitoring.
- Exact libraries and versions — ATS systems scan for specific names: XGBoost, PyTorch, dbt, SageMaker. “Machine learning tools” is invisible to both ATS and technical reviewers.
- GitHub or Kaggle profile — link to public repos or competition notebooks; it is the fastest way to let a technical reviewer see your code style.
- Scale — data volume (millions of rows, TB of data) and user/customer counts help interviewers judge whether your experience fits their environment.
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More resume examples & guides
See all resume examples by job, including the software engineer resume and accountant resume. Check how to pass automated screening on our ATS-friendly resume guide.
FAQ
What should a data scientist put on a resume?
Business outcomes (not just model metrics), the full ML pipeline you’ve owned, specific libraries and cloud platforms, data scale, and links to GitHub or Kaggle. A short summary that names your domain (e-commerce, fintech, healthcare) helps recruiters route you to the right role.
How long should a data scientist resume be?
One page for under 5 years; two pages for senior or staff data scientists. Focus each bullet on impact — a tight one-page resume beats a padded two-page resume at every career level below principal.
Do data scientist resumes need to show code?
Not on the resume itself, but link your GitHub or portfolio. Technical interviewers will look at your public repos before the onsite to gauge code quality and problem-solving approach.
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