Data Scientist ATS Keywords

Top ATS keywords for Data Scientist resumes — the ML techniques, statistical methods, and tools applicant tracking systems screen for.

Essential Keywords

Technical Skills

Machine LearningStatistical AnalysisPredictive ModelingA/B TestingExperimental DesignCausal InferenceFeature EngineeringTime Series ForecastingNatural Language ProcessingClassification & RegressionPythonSQL

Soft Skills

Data StorytellingStakeholder CommunicationBusiness AcumenCritical ThinkingCross-functional CollaborationPresentation Skills

Tools & Software

scikit-learnXGBoostPandasNumPyJupyterTensorFlowPyTorchTableauSparkSageMakerMLflowR

Certifications

AWS Certified Machine Learning — SpecialtyGoogle Professional Machine Learning EngineerMicrosoft Azure Data Scientist AssociateSAS Certified Data Scientist

Action Verbs for Data Scientist Resumes

ModeledPredictedAnalyzedExperimentedQuantifiedSegmentedForecastedDeployedValidatedUncovered

Industry Terms

AUC/ROCPrecision & RecallStatistical SignificanceHypothesis TestingCross-ValidationModel Interpretability (SHAP)Uplift ModelingPropensity ScoringCohort AnalysisLTV Prediction

How to Use These Keywords

Include model metrics (AUC, RMSE, precision/recall) next to each modeling keyword — they double as ATS terms and credibility signals.

Use both 'machine learning' spelled out and 'ML' — ATS systems don't always equate them.

Name experimentation terms explicitly (A/B testing, power analysis) — product companies filter on them heavily.

Match the role flavor: analyst-leaning postings want SQL/Tableau emphasis; ML-leaning ones want deployment terms like SageMaker and MLflow.

Add business impact vocabulary (revenue lift, churn reduction, LTV) — recruiter keyword searches increasingly include outcome terms.

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