Data scientists build predictive models, run experiments, and extract insights from large datasets to solve business problems. They combine statistics, programming, and domain knowledge. When you apply for Data Scientist roles, applicant tracking systems scan your CV for the skills, tools, and responsibility phrasing below. Use them in context within your experience bullets, not just in a skills list.
These are the skills that appear most frequently in Data Scientist job descriptions. The importance score reflects how heavily each skill is weighted in the occupation's O*NET profile. Include the top 5 in your CV where you can prove them with real outcomes.
| Skill | Importance | Type |
|---|---|---|
| Python |
95
|
technical |
| Statistical Modeling |
92
|
technical |
| Machine Learning |
90
|
technical |
| Problem Solving |
88
|
soft |
| SQL |
85
|
technical |
| Critical Thinking |
85
|
soft |
| Experiment Design |
82
|
technical |
| Communication |
80
|
soft |
| Data Visualization |
75
|
technical |
| Business Acumen |
75
|
soft |
Recruiters and ATS systems search for specific tool names. List the ones you have real experience with in your skills section, and prove them in your bullets. Tools marked as hot are currently in high demand in job postings.
These are the core tasks a Data Scientist is expected to perform. Use them as starting points for your CV bullets, but rewrite each one with your specific outcomes, scope, and context. Do not copy them verbatim.
Job postings for this role may use any of these titles. Search for all of them to find more relevant opportunities, and use the matching title in your CV's professional summary when applying.
Paste a job description and cvlinkd will place these keywords in your CV where the ATS and recruiter expect them.
Tailor my CV now →Explore ATS keywords for other roles in the Data & Analytics family:
Includes information from O*NET 29.x by the U.S. Department of Labor/Employment and Training Administration (ETA), used under the CC BY 4.0 license.