Data & AnalyticsSenior level

Data Scientist CV Example

Models in production with business deltas — data science past the notebook stage.

The credibility test for a data science CV is the word "production". Models that shipped, with baselines and business deltas, separate practitioners from notebook-owners. This example states, for every model: the baseline beaten, the deployment path and the money or behaviour moved.

Why this CV works

Every model has a baseline comparison ("versus the incumbent scorecard", "at unchanged approval rates") — the honest way to claim ML wins.
MLOps details (MLflow, drift monitors, caught incidents) prove production ownership, not handoff-and-forget.
Experimentation guardrails show statistical judgement applied to the whole org, a senior signal.
Business units vary across bullets — defaults, stockouts, budget efficiency — showing range across problem types.

The professional summary

Data Scientist with 7 years shipping models to production in e-commerce and fintech. Built the demand-forecasting system (LightGBM) that cut stockouts 27% across 12K SKUs, the credit-risk model that reduced defaults 18% at equal approval rates, and the uplift model that made a AED 4M retention budget 31% more efficient. Ships via MLflow + Airflow with monitored drift.

Name three shipped models with their business deltas. Leave architectures for the interview; the summary sells outcomes, not layers.

Writing the work experience

For every model: the metric it moved, against what baseline, at what scale, and how it is served. A model without a baseline comparison is an anecdote.

Turning duties into achievements

Instead ofBuilt machine learning models

WriteShipped a credit-risk model scoring 40K applications/month; defaults fell 18% at unchanged approval rates

Instead ofRan A/B tests

WriteIntroduced power checks and sequential-testing rules; false-win launches dropped to zero across a 15-person product org

Skills on this CV

Pythonscikit-learn / LightGBMSQLMachine learningFeature engineeringMLflowAirflowA/B experimentationModel monitoringPandas / NumPyUplift modellingStatistics & causal inference

ATS tips for Data Scientists

Include "machine learning" in full — "ML" alone under-matches — plus your model families (gradient boosting, uplift).
Name the MLOps tools (MLflow, Airflow); "model deployment" as a phrase also filters well.
Keep "Python" and "SQL" as separate skills even at senior level; they remain baseline filters.

ATS checks help identify potential compatibility issues, but employers and ATS platforms use different criteria — no template or score can guarantee an outcome.

Writing tips

"At unchanged approval rates" — always state what you held constant; it is what makes an ML claim rigorous.
Caught incidents (drift, degradation) are excellent bullets: they prove monitoring is real.
Skip Kaggle and coursework after your first shipped model; production evidence supersedes practice.