Data & Analytics - ATS keyword map
Data Scientist resume keywords for ATS in India
These are the role signals Indian recruiters and ATS parsers commonly expect in a Data Scientist resume. CVScan can compare your resume against these terms and show what is missing before you apply.
Top keywords to include
Machine Learning
Mention this in skills, projects, or experience only when you can explain how you used it.
Python
Mention this in skills, projects, or experience only when you can explain how you used it.
Scikit-learn
Mention this in skills, projects, or experience only when you can explain how you used it.
TensorFlow
Mention this in skills, projects, or experience only when you can explain how you used it.
SQL
Mention this in skills, projects, or experience only when you can explain how you used it.
Feature Engineering
Mention this in skills, projects, or experience only when you can explain how you used it.
A/B Testing
Mention this in skills, projects, or experience only when you can explain how you used it.
Tableau
Mention this in skills, projects, or experience only when you can explain how you used it.
Where to place keywords
- Professional summary: target role, domain, and strongest tools.
- Skills section: clean list of tools, platforms, frameworks, and certifications.
- Experience bullets: keyword plus action, result, scale, or metric.
- Projects: especially important for freshers, career switchers, and tech roles.
Avoid keyword stuffing
- Add Machine Learning inside a real project or work bullet, not only in the skills list.
- Mention tools like Python and Scikit-learn near outcomes, reports, releases, or responsibilities.
- Use the exact job-description spelling when it is truthful: ATS systems often match literal terms.
- Support every keyword with proof, such as project size, team size, revenue, time saved, volume handled, or accuracy improved.
Common missing keyword problems
No business impact metric for models
Missing model accuracy or improvement %
Listing libraries without problem context
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