Data Scientist resume example

What to put in the summary

Say which problems you model (forecasting, ranking, causal inference), which stack you ship with, and whether you take work from question to production. Reviewers want to know if you build things that are used, so say that plainly.

Data scientist working on demand forecasting and pricing. Five years taking models from notebook to scheduled production jobs in Python and Spark, with an emphasis on honest evaluation: holdout periods, baselines and a clear statement of what the model cannot do.

Sample bullets with numbers

State the baseline you beat. A metric with no comparison point (“accuracy of 91%”) invites doubt; a gain over a plain rule shows judgment.

• Replaced a seasonal-average baseline with a gradient-boosted demand model; weekly forecast error (WAPE) fell from 24% to 16% across 3,200 products.
• Designed and analysed a six-week pricing experiment on 60,000 customers; the winning variant lifted margin by 2.1% with no change in order volume.
• Moved forecasting from a laptop notebook to a scheduled pipeline with monitoring, so stale predictions trigger an alert instead of a surprise.
• Wrote the evaluation guide the team now uses: baseline first, time-based splits, and a one-page note on failure cases.
• Built a churn-risk score for the retention team and measured its lift against a simple recency rule; it beat the rule by 11% on top-decile capture.
• Cleaned and documented a 90-million-row events table used by four teams.

These numbers are invented to show the shape of a good bullet. Use your own, and leave a number out rather than guess.

Key skills to list

• Python
• SQL
• scikit-learn
• XGBoost
• PyTorch (basic)
• Spark
• Experiment design
• Causal inference
• Airflow
• Model monitoring
• Git
• Communicating uncertainty

Separate methods from tools if there is room. Name only what you have shipped or can explain to a skeptical engineer.

Common mistakes

1. 1Presenting notebook results as production impact.
2. 2Quoting accuracy without a baseline, class balance or time-based validation.
3. 3Listing every library in the scientific Python ecosystem.
4. 4Using “AI” and “deep learning” as filler when the work was gradient boosting, which is fine and often better.

A layout that suits this resume

Ledger numbers its sections in one column, which keeps a mix of experience, publications and projects easy to follow without sidebars. See the Ledger template, or compare all 22 layouts.