Resume keywords for a Data Scientist
Data science postings vary wildly — some are analyst roles with a fancier title, others are genuine modelling jobs. Read which one you are looking at before rewriting, because the keyword sets barely overlap and optimising for the wrong one wastes the application.
The keywords, grouped
Core
Modelling
Deep learning and LLM
Production
Statistics
Check your resume against a real posting
A checklist tells you what the market asks for. Paste an actual job description and your resume to see which of these terms you are already covering and which you are not — free, and the resume never leaves your browser.
Run the checkHow to evidence these on the page
- Report model performance in the metric the business cares about alongside the technical one: "lifted precision to 0.91, cutting manual review by 40%".
- Say whether the model reached production and how many predictions it serves. A model that never shipped is a project, and reviewers know the difference.
- Name the baseline you beat. "Improved accuracy" is unfalsifiable; "improved on the rules-based baseline by 18 points" is evidence.
- If the posting mentions LLMs or RAG and you have genuinely built retrieval over a document set, use the exact terms — this vocabulary is new enough that filters match it literally.
What gets these resumes rejected
- A resume of coursework and Kaggle notebooks with no deployed work, applied to a production-modelling role.
- Burying the statistics. Many teams screen harder on experimental design than on framework familiarity.
A caution about keyword stuffing
Every term above is worth including only if it is true of you. Padding a resume with unearned keywords gets you into interviews you then fail, which is worse than not being shortlisted. Use this as a prompt to surface experience you already have and forgot to write down — not as a list to copy.