Critical ATS keywords for data scientist resumes. Cover machine learning, statistics, and programming skills that applicant tracking systems scan for.
Include the ML framework from the job description prominently — 'PyTorch' and 'TensorFlow' are both commonly ATS-searched and not interchangeable.
Quantify model performance: 'Achieved 94% AUC-ROC' and 'Reduced prediction latency from 850ms to 120ms' are memorable and ATS-optimized.
List domain-specific experience: NLP, computer vision, time series, or recommendation systems — this filters for specialized roles.
Include production deployment experience if you have it — 'deployed to production' or 'serving 1M+ daily predictions' differentiates academic projects from real-world experience.
Data scientist resumes emphasize statistical analysis, model building, and business insights. ML engineer resumes emphasize production engineering: model serving, pipeline development, MLOps, and system scalability. Tailor based on where you sit on the research-to-engineering spectrum.
Yes, if you're in the top 10-15% or have won a competition. Include your ranking percentile or specific competition achievement. Kaggle demonstrates self-directed learning and practical ML ability, which is valuable for early-career data scientists without extensive work experience.
Very technical — but always tied to outcomes. Don't just list algorithms; show the business problem each model solved. 'Built churn prediction model (XGBoost, 89% AUC) that identified $2.3M at-risk ARR quarterly' is stronger than 'developed machine learning models.'
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