Data Scientist Resume Example (ATS-Optimized)
Real data science resume that passes ATS filters and lands interviews. ML models, impact metrics, and technical skills.
Data scientist resumes need a balance: show business impact, technical depth, and statistical thinking. This example emphasizes machine learning models, data infrastructure, and quantified business results that ATS systems and hiring managers look for.
Resume Example
Key Takeaways for Data Science Resumes
DS resumes should show: (1) ML models built with measurable business impact, (2) Data infrastructure and tooling, (3) Communication skills (explaining results to non-technical stakeholders), (4) Both breadth and depth. Include specific ML techniques (gradient boosting, neural networks) and tools (TensorFlow, PyTorch). Most importantly, quantify business impact—$2M fraud prevented, 18% increase in purchase value. ATS systems parse these metrics heavily for data roles.
Frequently Asked Questions
Should I include Kaggle competitions?
Yes if you ranked highly. Include rank and dataset size. Example: 'Ranked top 5% in Kaggle competition (40K+ competitors) for time-series forecasting.'
How much Python/SQL code should I show?
Don't include code, but reference key technologies. Example: 'Built ML pipeline (Python, scikit-learn, PostgreSQL) processing 100M+ records.' Recruiters will test technical skills in interviews.