Data Scientist resume example
A data scientist resume should show models that made it into the real world. Name the problem, the method, the data scale and, most importantly, the business metric that moved once the model was used.
This is a fictional, illustrative example -- not a real person or a promised result. Replace every detail with your own.
Full example resume
Sample content, for illustration -- not a real person.
Data scientist with 5 years of experience building forecasting and recommendation models in Python for e-commerce. Shipped a demand-forecasting model that cut stock-outs by 30% across 1,200 products.
- •Built a gradient-boosted demand forecast for 1,200 products, cutting stock-outs 30% and overstock write-offs by $900K a year.
- •Launched a product recommendation model that raised average order value 7% in an A/B test with 400,000 users.
- •Deployed models behind a FastAPI service serving 2M predictions a day with drift monitoring.
- •Ran a weekly experimentation clinic that helped 5 product teams design statistically sound tests.
- •Built a churn model in scikit-learn that flagged at-risk subscribers with 0.81 AUC; retention offers saved 3,000 subscriptions.
- •Moved reporting from spreadsheets to BigQuery, cutting query times from minutes to seconds.
- •Analysed 25 marketing experiments and presented results to the growth team.
Summary: weak vs. improved
Data scientist with expertise in machine learning, deep learning and AI.
Data scientist building forecasting and recommendation models for e-commerce. Shipped a demand forecast that cut stock-outs by 30%.
Buzzwords describe the field. A domain, model type and business result describe what you can do for this company.
Skills to include
- ✓Python (pandas, scikit-learn, PyTorch)
- ✓SQL and large datasets (Spark, BigQuery)
- ✓Statistics and experimentation
- ✓Machine learning (classification, forecasting, NLP)
- ✓Model deployment and monitoring
- ✓Communicating results to stakeholders
Only list skills you actually have -- the AI review in the editor will flag a skill that doesn't appear anywhere else in your resume.
Bullet examples: weak vs. improved
Built machine learning models for forecasting.
Built a demand forecast for 1,200 products, cutting stock-outs 30% and overstock write-offs by $900K a year.
Scale and dollar impact show the model actually mattered to the business.
Worked on a recommendation system.
Launched a product recommendation model that raised average order value 7% in an A/B test with 400,000 users.
A controlled test result is the most credible evidence of model value.
Deployed models to production.
Deployed models behind a FastAPI service serving 2M predictions a day with drift monitoring.
Throughput and monitoring show production engineering, not just notebooks.
How this changes by experience level
Lead with projects on real, messy data and your thesis or internship. Show the full pipeline, not just the model.
Built an NLP classifier on 120,000 support tickets during an internship, routing 60% automatically with 92% precision.
Show shipped models with business metrics and experimentation, like the full example above.
See the full example above.
Show platform thinking, cross-team impact and mentoring.
Set up the team’s shared feature store and model review process, cutting time-to-production for new models from 3 months to 3 weeks.
Section order
- Contact and links (GitHub, portfolio)
- Short summary
- Skills
- Experience
- Projects or publications
- Education
Common mistakes to avoid
- !Listing algorithms without saying what problem they solved
- !Model accuracy with no business outcome
- !Only Kaggle projects when you have real work to show
Recommended template: Technical
Skills matrix and project highlights for engineers.