ATS résumé checker for data scientists and analysts
Data résumés often read like a narrative, with the real toolset tucked into paragraphs. The skills are there, but they're easy for a screen, and a recruiter skimming the parsed profile, to miss.
How data scientists résumés score
From 252 anonymous checks classified as data. Counts only, never résumé text. See the State of the ATS
What trips up data scientists' résumés
Skills buried in paragraphs
One résumé in our test set listed Python, SQL, pandas, PyTorch, and Tableau only inside a first-person intro, with no Skills section at all. Keyword extraction can still find them, but a plain Skills section makes them impossible to miss for every parser.
Acronyms without the full term
Our parser counts ML as Machine Learning, but a posting's literal wording is what a screen matches. Write the full term once, then use the acronym.
Degree requirements
Many data postings name a degree. Our job-match check treats a degree in the posting as a hard filter, so spell out your degree in a standard Education section.
Dashboards pasted in as images
A screenshot of a Tableau dashboard is an image, so nothing in it can be read. Describe the result in words instead.
Keywords our parser recognizes for this field
Only list what you genuinely have, and use the wording of the posting you're applying to. A free check shows which of the posting's terms your résumé is missing.
Headings that map cleanly
Each of these maps to a standard field. If a heading on your résumé doesn't, the check flags it.
Know which ATS you're facing
Different platforms read the same file differently. Read the guide for the one on your application.
See exactly what the bot pulls from your résumé
Upload your résumé and paste the job posting. You'll see every field the parser extracts, every problem above that applies to you, and the posting's terms you're missing.
Check my résumé, free