Data Scientist resume example. Plus the keywords recruiters search for.
A data scientist resume that ties models to business results — with the machine learning, statistics and tooling keywords hiring teams filter on.
Reviewed by Nazir Elmurodov · Updated
/ The example
Data Scientist resume example.
Arjun Mehta
Data Scientist
San Francisco, CA · arjun.mehta@email.com · github.com/amehta
Summary
Data scientist with 5 years in e-commerce and fintech. I build and ship models for ranking, fraud and forecasting — and measure whether they move the business.
Experience
Data Scientist, Litware Marketplace · Mar 2022 – Present
- • Built a gradient-boosted ranking model for recommendations that lifted click-through by 8% in an A/B test
- • Design and analyze 20+ experiments a year using a shared Python framework for power analysis
- • Deployed batch and real-time models with MLflow and Docker, with automated drift monitoring
- • Cut weekly model retraining from 6 hours to 40 minutes by moving feature pipelines to Spark
Junior Data Scientist, Pennant Pay · Jul 2020 – Feb 2022
- • Built a fraud model that caught 23% more fraudulent transactions at the same false-positive rate
- • Wrote SQL and Python pipelines feeding 15 production dashboards
Education
M.S. Statistics, University of California, Berkeley, 2020
Skills
Python · SQL · scikit-learn · XGBoost · PyTorch · Spark · A/B testing · Causal inference · MLflow · AWS SageMaker
Arjun Mehta
Data Scientist
arjun.mehta@email.com · San Francisco, CA
github.com/amehta
Summary
Data scientist with 5 years in e-commerce and fintech. I build and ship models for ranking, fraud and forecasting — and measure whether they move the business.
Experience
Data Scientist — Litware Marketplace
Mar 2022 – Present
• Built a gradient-boosted ranking model for recommendations that lifted click-through by 8% in an A/B test
• Design and analyze 20+ experiments a year using a shared Python framework for power analysis
• Deployed batch and real-time models with MLflow and Docker, with automated drift monitoring
• Cut weekly model retraining from 6 hours to 40 minutes by moving feature pipelines to Spark
Junior Data Scientist — Pennant Pay
Jul 2020 – Feb 2022
• Built a fraud model that caught 23% more fraudulent transactions at the same false-positive rate
• Wrote SQL and Python pipelines feeding 15 production dashboards
Education
University of California, Berkeley
M.S. Statistics · 2020
Skills
Python, SQL, scikit-learn, XGBoost, PyTorch, Spark, A/B testing, Causal inference, MLflow, AWS SageMaker
Why it works
Each bullet names the model or method and the business metric it moved, experiments are described with real rigor, and the tools appear exactly as data science job posts write them.
Use this as your starting point
Opens in the editor with every section filled in. Swap in your own details and download a PDF or Word file free.
Start from this exampleSample resume. Names, companies and figures are illustrative.
/ Summary examples
Three summary examples. Pick the one closest to you.
Two or three lines: your level, your field and one result.
- Entry level
Statistics graduate with an M.S. and a capstone forecasting hospital admissions. Skilled in Python, SQL and scikit-learn, with two end-to-end modeling projects on GitHub.
- Mid level
Data scientist with 5 years in e-commerce and fintech. My ranking and fraud models lifted click-through by 8% and caught 23% more fraud.
- Senior
Senior data scientist who has led a team of 4 and set modeling and experimentation standards for a marketplace with millions of monthly users.
More for other jobs and situations (no experience, career change, senior): resume summary examples.
/ Bullet points
Bullet point examples. From duty to result.
Turn duties into results: what you did, how, and what changed.
Modeling
Instead of: Built machine learning models
Write: Built a ranking model that lifted recommendation click-through by 8% in an A/B test
Experiments
Instead of: Ran A/B tests
Write: Designed and analyzed 20+ experiments a year with a shared power-analysis framework
Production
Instead of: Deployed models
Write: Deployed batch and real-time models with MLflow and Docker, with automated drift alerts
Efficiency
Instead of: Improved data pipelines
Write: Cut model retraining from 6 hours to 40 minutes by moving feature pipelines to Spark
/ Keywords
Keywords and skills for data scientists.
These come up often in data scientist job posts. Include only the ones you can back up in an interview, and use the exact wording from the post you're applying to.
- Python
- R
- SQL
- Machine learning
- Statistics
- scikit-learn
- TensorFlow
- PyTorch
- XGBoost
- A/B testing
- Causal inference
- Feature engineering
- Deep learning
- NLP
- Spark
- MLflow
- AWS
/ ATS tips
ATS tips for data scientists.
Lead with impact, then method
"Lifted click-through by 8% with a ranking model" reads better than a list of algorithms.
Show models in production
Deployment, monitoring and retraining set you apart from purely exploratory work.
Match the flavor of the role
Some posts are analytics-heavy, others are closer to ML engineering — mirror the mix of statistics, SQL and engineering they ask for.
Write links as text
GitHub, Kaggle or a blog belong in the contact line as plain URLs, with the results described on the resume itself.
How does your data scientist resume compare?
Free ATS score, missing keywords and fixes in about 10 seconds.
/ Questions
Data Scientist resume FAQ.
Writing the cover letter too? See Arjun's cover letter, annotated
How is a data scientist resume different from a data analyst resume?
Data analyst resumes center on SQL, BI and reporting. Data scientist resumes add modeling, experimentation and getting models into production.
Do I need a master's or PhD?
Many roles prefer one, but strong results with models in production can matter as much. Put education first only if it's your strongest point.
Should I list every library I've used?
No. List the ones you'd be comfortable discussing in depth, led by those in the job post.