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Data Scientist Resume Example (2026)

Data scientist resumes win on shipped models and business outcomes, not Kaggle scores. The example below names the model in production, the metric the model moves, and the deployment path that got it there.

Check my data scientist resume See Data Scientist keywords

Sample resume

Data Scientist resume — full example

One-column, ATS-friendly layout. Names and numbers are illustrative.

Dr. Theo Brennan

Chicago, IL · theo.brennan@example.com · linkedin.com/in/theobrennan

Senior Data Scientist

Professional Summary

Senior data scientist with 6 years shipping production ML at consumer and B2B companies. Built the demand-forecasting model now driving $22M of annual inventory spend at a publicly-traded retailer; ran the experiment program that converted three pricing changes worth $4.8M ARR. Strongest in causal inference, time-series, and the operational discipline around getting models into production.

Technical Skills

  • Languages: Python (pandas, scikit-learn, statsmodels, PyTorch), SQL, R
  • Modeling: Regression, Classification, Time-series (ARIMA, Prophet, neural), Causal inference (synthetic controls, DiD, IV), Gradient boosting (XGBoost, LightGBM)
  • MLOps: MLflow, Airflow, Kubeflow, Feature stores (Feast), Model monitoring
  • Data: Snowflake, BigQuery, Postgres, dbt, Spark
  • Experimentation: A/B testing, Switchback, Bandits, Statistical power, Holdout design

Professional Experience

Senior Data Scientist — Riverside Retail

Chicago, IL · May 2023 — Present

  • Built the SKU-level demand-forecasting model (LightGBM + Prophet ensemble) now driving $22M of annual inventory spend; cut over-stock 18% and stock-outs 11% across the top 4 categories in the first year.
  • Owned the experiment program: scoped 24 tests, wrote the analysis playbook the team now uses, and shipped 6 wins compounding to $4.8M ARR over 18 months.
  • Replaced the legacy churn model (logistic regression) with a hand-tuned XGBoost pipeline + SHAP explainability; ROC AUC moved from 0.71 to 0.84 and the model became the single biggest driver of save-team prioritization.
  • Built the team's first reproducible training pipeline (MLflow + Airflow + dbt); cut time from experiment to production model from 6 weeks to 9 days per the team retro.

Data Scientist — Tinder Bow Health

Remote · Oct 2020 — Apr 2023

  • Built a readmission-risk model from claims data (gradient boosting + survival analysis); ROC AUC 0.78, identified 11,400 high-risk patients in year one, contributing to a 14% reduction in 30-day readmissions on the targeted cohort.
  • Designed the causal-inference framework used to evaluate four non-randomized care interventions (synthetic controls + DiD); two interventions were de-prioritized after the framework showed weaker effects than the previous descriptive analysis claimed.
  • Mentored 4 junior data scientists; co-authored the team's 'how to write a model card' guide adopted org-wide.

Education

  • Ph.D. Statistics — University of Michigan · 2020 · Dissertation on causal inference for non-randomized clinical interventions
  • B.S. Mathematics — University of Wisconsin · 2015

Why this resume works

  • Every model bullet names the technique (LightGBM, XGBoost, gradient boosting + SHAP) so JDs asking for those frameworks get exact matches.
  • 'Drove $22M annual inventory spend' anchors the model to business value — the single most important framing for data scientist resumes.
  • Production tooling (MLflow, Airflow, Feast) is named explicitly — modern senior DS roles almost always require evidence of model deployment, not just notebook experimentation.
  • Causal inference (synthetic controls, DiD, IV) is named at the technique level — JDs that specify these as nice-to-haves get a direct keyword hit.
  • Education at the bottom in two lines, despite a PhD. Recruiters expect it there; the dissertation gets one specific clause that explains relevance.

Common mistakes

  • Listing Kaggle competitions as proof of skill. Hiring managers screen these out — Kaggle ranking is not predictive of production work.
  • Saying 'experience with machine learning'. Name the model family (gradient boosting, transformers, time-series), the production path (pipeline, monitoring), and the business outcome.
  • Burying business impact. '$22M annual spend driven', '$4.8M ARR from experiments', 'reduced readmissions 14%' belong in the first 5 words of the bullet.
  • Listing 20 Python packages. Pick the 5-7 you use heavily in production. The keyword-stuffing detector flags long inventories.

ATS keywords

Data Scientist resume keywords ATS systems scan for

A condensed view. For the full categorized list, see the Data Scientist keywords page.

Hard skills

  • Machine learning
  • Deep learning
  • Statistical modeling
  • Causal inference
  • Time-series forecasting
  • Regression
  • Classification
  • Clustering
  • Dimensionality reduction
  • Feature engineering
  • Hyperparameter tuning
  • Model validation

Tools

  • Python
  • R
  • SQL
  • pandas
  • NumPy
  • scikit-learn
  • statsmodels
  • XGBoost
  • LightGBM
  • CatBoost
  • PyTorch
  • TensorFlow

Action verbs

  • Built
  • Designed
  • Trained
  • Deployed
  • Tuned
  • Validated
  • Identified
  • Improved
  • Reduced
  • Increased
  • Forecasted
  • Quantified

Soft skills

  • Cross-functional collaboration
  • Stakeholder communication
  • Storytelling with data
  • Executive presentation
  • Mentorship
  • Model card authorship
  • Documentation
  • Project scoping
See the full Data Scientist keyword list

Questions

Data Scientist resume — frequently asked

Do I need a PhD for senior DS roles?+
No. A PhD signals research depth but a strong production ML record without one is more valuable to most hiring managers in 2026. Lead with shipped work; the degree goes at the bottom.
How should I list models I built that didn't ship?+
Frame as research outcomes: 'Built a churn model with 0.84 AUC; recommended against deployment after PSI drift analysis showed unstable population predictions.' Judgment about what NOT to ship is a senior signal.
Should I mention LLM / generative AI work in 2026?+
If you've shipped a feature with one in production, yes — name the model and the eval methodology. If you've just experimented in a notebook, leave it out. The bar for claiming GenAI experience has risen sharply.
Kaggle ranking — list it or not?+
Generally not. Hiring managers either ignore it or screen it as a yellow flag. Replace with one specific production model and its business outcome.

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