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Data Scientist ATS Keywords

Include these keywords in your CV to pass Applicant Tracking Systems and increase your chances of getting seen by recruiters.

Data & Analytics

Which keywords should a Data Scientist CV include?

Use the exact words from the job advert first: applicant tracking systems match terms, not meanings. For Data Scientist roles the essential ones are Python, Machine Learning, SQL, Statistics. Put them in your profile, skills list and experience bullets, written as they appear in the advert, and only where they are true for you, in an ATS-friendly CV template.

As of October 2026. Updated .

4Essential
4Recommended
0Nice to Have

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Essential Keywords

Must have

These keywords are critical for Data Scientist roles. Missing these may result in automatic rejection.

PythonMachine LearningSQLStatistics

Recommended Keywords

Should have

Including these keywords will strengthen your application and improve your ranking.

TensorFlowAWS SageMakerNLPA/B Testing

Keywords by Category

Technical Skills

PythonMachine LearningSQLStatisticsNLPA/B Testing

Tools & Software

TensorFlowAWS SageMaker

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How to Use These Keywords

1

Integrate Naturally

Weave keywords into your achievement statements rather than listing them separately.

2

Match the Job Description

Use the exact phrasing from the job posting when it matches your experience.

3

Include Variations

Use both acronyms and full terms: "Search Engine Optimisation (SEO)"

4

Prioritise Relevance

Only include keywords that genuinely reflect your skills and experience.

Data Scientist CV FAQs

Include "machine learning", "deep learning", "neural networks", "NLP", "computer vision" if applicable. Mention specific algorithms (random forest, gradient boosting, regression) and frameworks (TensorFlow, PyTorch, scikit-learn).
Yes, if you have experience. Include "Spark", "Hadoop", "distributed computing", "data pipelines", and cloud data tools (BigQuery, Redshift, Databricks). These are increasingly important for data science roles.
Python is essential - include it prominently along with libraries (Pandas, NumPy, Matplotlib, Seaborn). Also mention R, SQL, and any other languages you use regularly.
Very important. Include "business insights", "stakeholder communication", "A/B testing", "experimentation", and industry-specific terms. This shows you can translate technical work into business value.

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