ATS Keywords
Data Scientist ATS Keywords: What Recruiting Software Is Actually Scanning For
Most Data Scientist applications are filtered by an Applicant Tracking System before a human ever opens the CV, and that filter is matching text, not judging talent. Getting through means using the specific tools, statistical methods, and phrasing that appear in the job description, not synonyms or vague competency claims. This page lists the exact keywords, phrases, and formatting choices that move a Data Scientist CV from the auto-reject pile into the interview pile.
Optimize Your CVATS Optimisation
Must-Have Keywords
Include these keywords in your CV to improve your ATS score.
Strengthen Your CV
Power Phrases
Use these multi-word phrases to strengthen your CV.
Avoid These
Words to Avoid
Remove these overused or weak words from your CV.
ATS Score
Before & After ATS Optimization
Before
Missing Keywords
After
Added Keywords
Formatting
ATS-Friendly Format Tips
Use standard section headers like Experience, Education, and Skills. Creative labels such as 'My Journey' often get mis-parsed or dropped entirely.
Avoid tables, text boxes, and multi-column layouts for core content. Many ATS parsers read left-to-right and scramble or skip text trapped in a table cell.
Export as a text-based PDF or .docx, never a scanned image. Image-based PDFs return blank or garbled text to the parser.
Spell out acronyms at least once, such as 'Natural Language Processing (NLP)', since the ATS may be searching for the abbreviation, the full term, or both.
Mirror the exact terminology from the job posting, like 'scikit-learn' vs 'sklearn' or 'SQL' vs 'Structured Query Language', since ATS matching is closer to exact string match than synonym-aware search.
List tools and libraries as plain text in a dedicated Skills section, not as logo icons or graphics, which most parsers cannot read at all.
Stick to standard fonts like Arial, Calibri, or Times New Roman, and keep critical content out of headers and footers, which some parsers skip entirely.
FAQ
Frequently Asked Questions
Yes. Most enterprise ATS platforms rank or filter CVs by keyword match before a recruiter sees them, so a strong candidate whose CV says 'predictive analytics' when the job description says 'machine learning' can be scored below a weaker match who used the exact term.
No. List the ones relevant to the job description and back each with a result. A long, undifferentiated tool list can trigger keyword-stuffing detection in some modern ATS platforms and reads poorly to the human reviewer who eventually opens the CV.
It backfires more often than it helps. Modern ATS platforms and the recruiters who calibrate them increasingly penalize unnatural repetition, and a CV that reads as a keyword dump gets desk-rejected once a human sees it.
You need to adjust the keyword emphasis for each posting, not rewrite the whole CV. Keep a master CV with your full project and skill inventory, then mirror the specific tools and terms from each job description before submitting.
It can. Most modern ATS platforms parse both correctly, but a small share of older systems handle .docx more reliably than PDF. When in doubt, submit the format the application portal explicitly recommends.
Aim to naturally include the majority of the hard-skill keywords, such as tools, methods, and languages, that appear in the job description, each backed by a specific example, rather than hitting a specific percentage.
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