The summary
Three sentences: your years and the stack, the one model that moved a business number, and the kind of problem you want. Data science is hired on impact, so the number goes early — not the algorithm names.
- Lead with a model that changed a metric (churn, revenue, stockouts), not a list of frameworks.
- Say you ship to production — "deployed", "in production" — it separates you from notebook-only candidates.
- Name Python and one or two real tools; save the rest for the skills section.
- Drop the buzzwords; "cut churn 18%" beats "leveraged cutting-edge AI".
Works
Data scientist with 5 years building and shipping ML models in Python. Deployed a churn model that cut monthly churn 18% and a forecast that reduced stockouts 30%.
Does not
Passionate data scientist skilled in machine learning and AI, seeking to leverage advanced analytics for a forward-thinking company.
Experience bullets
Each bullet is a verb, the model or analysis, and the business number it moved — revenue, churn, accuracy, conversion, hours saved. Tie the model to the outcome; the outcome is what gets read.
Works
Built and deployed an XGBoost churn model that cut monthly churn 18%, worth ₹6 crore a year
Works
Ran 40 A/B tests on the recommendation engine, lifting click-through 12%
Works
Shipped a demand forecast into production that cut stockouts 30% across 12 warehouses
Does not
Worked on various machine learning models and performed data analysis using Python.
Skills worth listing
Group so it reads at a glance: languages, the ML you know, the data tooling, and how you deploy. Deployment skills matter — list them; they are what move you past notebook-only candidates.
- Languages
- PythonSQLR
- Machine learning
- scikit-learnXGBoostTensorFlowPyTorchNLPA/B testing
- Data
- PandasSparkAirflowdbtBigQuery
- Deployment
- DockerAWS SageMakerMLflowFastAPI
Format
- One page until about eight years; the résumé is a highlight reel of shipped models, not every project.
- Single column, standard headings, so applicant tracking systems parse it cleanly.
- Link a GitHub or Kaggle; for data science, shipped work beats any list of frameworks.
- Put a model's business result first and the algorithm second — recruiters scan for impact.