Data Scientist
Use These Keywords
machine learning, Python, scikit-learn, feature engineering, model evaluation, A/B testing
Avoid Generic Terms
data enthusiast, AI passionate
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Data Science Resume Keywords for ATS: ML, NLP & Analytics (2026)
Data science roles need ATS keywords covering ML frameworks, statistical methods, business impact, and Python/R tooling.
Quick Answer
Combine ML framework names, statistical methods, business outcome metrics, and language/tool proficiency for best ATS coverage.
Key Takeaways
- Name ML frameworks (TensorFlow, PyTorch, scikit-learn) explicitly.
- Statistical methods (regression, hypothesis testing, Bayesian) signal depth.
- Business impact metrics make data science keywords credible.
Action Steps
- List all ML/AI frameworks and libraries you have used in production.
- Name statistical methods alongside the business problems they solved.
- Include deployment context: batch inference, real-time API, or dashboard.
Diagnostic Checklist
- ML frameworks listed with version or use context.
- Statistical methods appear alongside business problems solved.
- Programming languages include use context, not just listed.
- Model metrics are included: accuracy, F1, AUC, business lift.
- Deployment context mentioned: API, batch, dashboard, etc.
Role-wise Keyword Clusters
ML Engineer
Use These Keywords
TensorFlow, PyTorch, MLOps, model deployment, real-time inference, feature store
Avoid Generic Terms
ML expert, deep learning lover
NLP Engineer
Use These Keywords
NLP, transformer models, BERT, text classification, sentiment analysis, Hugging Face
Avoid Generic Terms
NLP researcher, language enthusiast
Continue Reading Path
Follow this guided reading path to build topic depth and improve your ATS outcomes faster.
FAQs
Should I include Kaggle projects on a data science resume?
If you lack production ML experience, yes. If you have production work, lead with that and mention Kaggle briefly.
Next Best Step
Use our tools to apply this guide and improve your next application.
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