DATA SCIENTIST RESUME

A data scientist resume
that shows real business impact

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What Hayven does to your bullets

DATA SCIENTIST · BEFORE & AFTER

❌ BEFORE

Built machine learning models to help the team make better business decisions.

✓ AFTER HAYVEN

Built a churn prediction model (XGBoost, 91% AUC) that identified at-risk accounts 30 days earlier — enabling targeted outreach that reduced churn by 18% and saved $2.3M ARR annually.

Keywords ATS systems look for in DS resumes

PythonSQLMachine LearningDeep LearningTensorFlowPyTorchSparkA/B TestingStatistical ModelingData PipelineDatabricksSnowflakescikit-learnNLPFeature Engineering

How to write a data scientist resume

Model performance metrics are required

AUC, F1, precision/recall, RMSE — always include model performance metrics. "Built a model" means nothing. "Built a model with 91% AUC" means everything.

Business impact over technical complexity

Hiring managers care more about what the model did for the business than how it worked. Lead with the outcome, then mention the method.

Stack and tools matter a lot

Python, SQL, R, TensorFlow, PyTorch, Spark, Databricks, Airflow, dbt, Snowflake — ATS systems for DS roles scan for these. List them in a dedicated Skills section.

Scale gives context

How large was the dataset? How many predictions per day? How much compute? Scale tells recruiters whether you can handle their data environment.

Deployed vs. prototype

Distinguish between models you deployed to production and experiments that stayed in notebooks. Production experience is significantly more valuable.

Domain expertise compounds

Healthcare, fintech, e-commerce, NLP, computer vision — domain-specific DS experience is a major differentiator. Name your domain explicitly in your summary.

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