What this role’s resume is actually judged on — the language postings use, what a reviewer looks for in the first ten seconds, and the specific way Data Scientist resumes go wrong.
A first pass takes seconds and is looking for three things. If they are not near the top, the rest of the page rarely gets read.
Most resumes fail one bullet at a time. The difference is almost always specificity — a number, a constraint, or a consequence.
Built machine learning models to predict customer churn.
Designed the retention experiment that showed the discount offer paid back only for accounts over 18 months old, redirecting £180k of annual spend.
Terms that recur in real postings for this role. They belong in the bullet that proves them, not in a list at the bottom — our own checker weights requirement coverage at 40% and raw keyword matching at 20%, and most serious systems make a similar trade.
A keyword you cannot defend in an interview is worse than a missing one. How the format checks work.
'Achieved 0.89 AUC' tells a hiring manager nothing about value — plenty of accurate models are never deployed and change nothing. Lead with the decision your work changed and keep the model metric as supporting detail.
Expect experiment design and causal reasoning over model trivia. The recurring trap is a question where the data cannot support a confident answer — saying so, and stating what you would need, is the mark.
Listing tools instead of questions answered
No mention of what happens when a pipeline breaks
Research projects presented as production work
No evaluation story anywhere on the page
Reading as an analyst who happens to write dbt
Listing technologies you have touched once
Generic resume advice only goes so far — what matters is whether this resume covers this posting. Paste both and get requirement-by-requirement coverage, the keywords you are missing, and the format problems a parser will hit. The first check is free.