HomeData Scientist ATS Keywords

ATS Keywords for Data Scientist Resumes

Data scientist resumes are judged on business impact, not model complexity. A well-tuned model that never shipped is worth less than a simple one that changed a real metric — lead with the latter.

Hard Skills

Machine LearningStatistical ModelingA/B TestingPython/RFeature EngineeringDeep Learning

Soft Skills

Stakeholder StorytellingExperiment DesignCross-functional Partnership with Product/Eng

Tools & Certifications

scikit-learn/TensorFlow/PyTorchSQLJupyterMLflowAWS SageMakerTableau/Looker

How to use these keywords

  • Lead every project with the business metric it moved (revenue, retention, conversion), not the model architecture used.
  • Mention A/B testing and experiment design explicitly — it signals rigor beyond just building models.
  • If a model shipped to production, say so; a model that stayed in a notebook is a very different accomplishment.

See recommended resume templates for Data Scientist

Curated templates that pair well with this role's keyword profile.

Frequently asked questions

Should I include Kaggle competition results?

Only if you're early-career and lack production experience — once you have shipped work, real business impact outweighs competition rankings.

How technical should the resume be for a non-technical hiring manager?

Lead with plain-language impact, keep technical depth in a skills section — many data science resumes are first screened by recruiters, not scientists.