The Data Scientist résumé, without the buzzword cloud.
DS résumés are infamous for skill-list overload — "Python · R · SQL · Tableau · Spark · Hadoop · TensorFlow · PyTorch · ..." Recruiters glaze over before they reach the experience. This template inverts it: one tight skills block, three substantial role bullets, every claim verifiable.
The full sample
Fictional candidate. Use as a structural model — swap in your real work.
Dr. Marcus Chen
marcus.chen@example.com · 555-0167 · Boston, MA · github.com/mchen-ds · linkedin.com/in/mchen-ds
Summary
Senior Data Scientist with 5 years applied work in subscription forecasting and causal inference, plus a PhD in Statistics. Strongest at making ML models survive production (drift monitoring, retrain cadence) and at the harder problem of getting stakeholders to act on the output.
Skills
Modelling: Causal inference (DiD, IV, synthetic control), forecasting (Prophet, ARIMA, neural), classical ML (XGBoost, scikit-learn), basic deep learning (PyTorch)
Senior Data Scientist@ Northstar SaaS2022 — Present
Built and shipped the churn-prediction model (XGBoost, weekly retrain) used to drive the retention team's outreach queue; out-of-sample AUC of 0.83, and intervention-arm A/B test showed a 1.6pp lift in 90-day retention (n = 12,400).
Re-designed the upsell-attribution analysis using a Difference-in-Differences setup that handled the staggered rollout; corrected a +$2.1M ARR over-attribution that the prior simple-uplift model had inflated.
Wrote and shipped MLflow + dbt monitoring layer for all four production models; reduced silent-failure incidents from ~2/month to 0 over the following 8 months by surfacing drift before stakeholders noticed.
Forecasted weekly patient volume across 23 outpatient clinics using a hierarchical Prophet model; MAPE of 6.2% beat the prior method by ~3pp and let operations cut staffing costs by ~$280K annually.
Built the "treatment-effect" causal inference framework (DiD + propensity weighting) used by 3 product teams for feature-launch evaluation; standardized what had been ad-hoc per-team analyses.
Stack: Python, R, Prophet, Postgres, Tableau
Education
Ph.D. Statistics — Boston University, 2020 · Dissertation on synthetic-control methods for irregular treatment timing.
B.S. Mathematics — University of Michigan, 2015
Why these bullets work: XYZ for ML work
DS résumés often fail in a specific way: they describe what was built (X) but not what it changed (Y). Every bullet here pairs the model/analysis with its decision-grade outcome — the metric a stakeholder cared about, not just the model metric.
X · the model or analysisY · the impact metricZ · the method or rigor
Bullet 1 — Production ML
Built and shipped the churn-prediction model (XGBoost, weekly retrain) used to drive the retention team's outreach queue; out-of-sample AUC of 0.83, and intervention-arm A/B test showed a 1.6pp lift in 90-day retention (n = 12,400).
Why it lands: Pairs the model metric (AUC) with the business metric (retention lift, with sample size). DS recruiters and hiring managers know AUC without retention is just curve-fitting; this bullet shows you closed the loop.
Bullet 2 — Causal inference
Re-designed the upsell-attribution analysisusing a Difference-in-Differences setup that handled the staggered rollout; corrected a +$2.1M ARR over-attribution that the prior simple-uplift model had inflated.
Why it lands: "Caught a $2.1M ARR over-attribution" is the kind of bullet that gets you interviews because it shows you correct organizational mistakes, not just produce numbers. Naming the technique (DiD with staggered rollout) signals real methodological depth.
Bullet 3 — MLOps
Wrote and shipped MLflow + dbt monitoring layer for all four production models; reduced silent-failure incidents from ~2/month to 0 over the following 8 monthsby surfacing drift before stakeholders noticed.
Why it lands: DS roles increasingly want ML engineering hybrids. Quantifying ops impact ("2/month to 0") signals you understand that the model only matters if it stays healthy in production.
DS-specific tactics
Name the technique, not just the framework
"Used PyTorch" tells a recruiter you typed an import statement. "Trained an XGBoost model with class-balanced sampling and SHAP-based feature pruning" tells them you made specific methodological choices. The second is harder to write but signals real depth.
Pair every model with its business decision
"Achieved 0.91 AUC" is interesting; "achieved 0.91 AUC, used by retention team for outreach prioritization, lifting 90-day retention 1.6pp in A/B test" is hireable. The model metric without the business outcome reads as kaggle work, not production work.
Add Klepify free. We'll rewrite bullets to surface the JD's must-have methods (causal inference, MLOps, deep learning) — where your real work supports them.