Interview prep built only for data roles.
Interview prep is built for software engineers and stretched over everyone else. A data loop is different: SQL under time pressure, a case study with no clean answer, metric definitions, experiment design, ML theory for some roles and pipeline design for others, and the mix changes with the title on the job post.
So candidates prepare from a list of questions and hope it was the right list. Nothing tells you, before the real interview, which skills this job will test, how ready you are on each, or what to do about the weakest one this week. Most people find out in the room.
DataPrepAI exists to answer those three questions before the interview, not after. Every part of the product follows one rule: measure first, then practice what the measurement says.
Upload a resume and the job description. The gap analysis scores the fit skill by skill and shows what that loop will lean on. Mock interviews, typed or spoken, are scored against a rubric and the feedback names what to fix. A readiness score per skill moves with your results, and the roadmap reorders itself around the weakest one. Run a second analysis for a similar role and your ratings carry over instead of starting from zero.
One audience. Six data roles, 108 skills in the catalog, and every question, rubric and roadmap written for them. There is no generic question bank underneath.
Scored, not graded by feel. Answers are marked against the same rubric every time, so a 62 this week and a 74 next week mean something.
Honest about the model. Results are generated by AI and checked against a fixed rubric, and the product says when a score is an estimate rather than a measurement.
Small by design. An independent product, changed weekly, with a real person reading every message.