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Data ScientistData Scientist CV example (2026)
A data scientist CV opens with a summary naming your domain (fraud, growth, forecasting) and core stack, then experience where each bullet ties a model or analysis to a business number. Recruiters skim for Python/SQL plus evidence you shipped something to production — not a list of Kaggle medals. Keep a Skills section with exact library names (pandas, scikit-learn, XGBoost) so the ATS scores them; education matters more here than in most tech roles, so give it a real section.
What recruiters scan for in a Data Scientist CV
- 1Business impact of models: revenue, churn reduction, fraud caught, forecast error
- 2Production evidence — 'deployed', 'monitored', 'retrained' — not just notebooks
- 3Exact stack names: Python, SQL, scikit-learn, Spark, dbt, Airflow
- 4Statistical literacy: experiments, significance, sample sizes
Example Data Scientist CV
A condensed but realistic example — note how every bullet pairs an action with a number, and how tools and methods are named exactly. Details are fictional.
Priya Raghavan
Data Scientist · Experimentation & ML
Amsterdam, NL · priya.raghavan@email.com · linkedin.com/in/praghavan
Summary
Data scientist with 5 years across growth analytics and production ML at marketplace companies. Built the experimentation platform used by 12 product teams; strong Python/SQL, causal inference, and stakeholder communication.
Experience
Data Scientist II
2022 – present
Vondel Market (marketplace)
- Built a gradient-boosted churn model (XGBoost, 1.4M users) that powers win-back campaigns; incremental retention worth ~€800k/year in a holdout test.
- Designed and standardised the company's A/B testing methodology (power analysis, CUPED variance reduction); false-positive rate on shipped experiments dropped measurably in audit.
- Deployed models via Airflow + MLflow with weekly retraining and drift alerts; zero silent-degradation incidents since rollout.
- Partnered with pricing team on demand elasticity analysis that informed a fee change worth +2.1% take rate.
TravelZoom
- Owned funnel analytics in SQL/dbt; identified a checkout step causing 14% mobile drop-off, fixed in one sprint.
- Automated weekly executive reporting in Python, saving ~6 analyst-hours per week.
Skills
- Python (pandas, scikit-learn, XGBoost)
- SQL / dbt
- Experiment design & causal inference
- Airflow / MLflow
- Spark
- Tableau / Looker
Education
MSc Statistics, University of Amsterdam, 2020 · BSc Mathematics, IIT Madras, 2018
ATS keywords for Data Scientist roles
These terms appear most often in Data Scientist job descriptions and carry the most ATS score weight. Make sure the ones you can honestly claim appear in your CV — in the exact phrasing.
- 1Python (pandas, NumPy, scikit-learn)
- 2SQL and relational databases
- 3Machine learning model development
- 4Statistical analysis and hypothesis testing
- 5Data visualisation (Tableau, Matplotlib, Seaborn)
See how your Data Scientist CV scores
An example shows the target — the free ATS checker shows your distance from it: score, keyword gaps, and the fixes ranked by impact. And when you're applying to a specific job, the AI Adapt flow rewrites your CV against that exact posting — honestly, without inventing experience. $4 to download.
Data Scientist CV FAQ
Should a data scientist CV include Kaggle or personal projects?
Only when you lack production experience — then one or two substantial projects with real data and a deployed artefact beat ten notebook competitions. Once you have industry bullets with business impact, projects drop off.
How technical should the bullets be?
Name the method and library, then translate to business outcome: 'XGBoost churn model → €800k retained revenue'. A bullet that's only math or only business is half a bullet; recruiters and hiring managers read the same page.
Does education matter on a data scientist CV?
More than in most tech roles — many JDs still filter on quantitative degrees. List degree, field, and university; add relevant coursework only if you're early-career.