Data Science Mock Interview

This is not a tips session. It's a full simulation — run the same way I evaluate candidates as a trained Bar Raiser with 200+ interviews conducted.

You'll get the real experience under real pressure, with honest feedback on exactly where you stand.

What the session covers:

→ ML/Statistics round — model selection, bias-variance tradeoff, evaluation metrics, probability, and the follow-up questions that trip most candidates up

→ Coding/SQL round — Python problem-solving and SQL window functions at the level senior DS roles actually test

→ System design / case study — designing an ML pipeline end-to-end, from problem framing to deployment considerations

→ Experimentation round — A/B testing design, statistical significance, metric selection, and how to handle inconclusive results

→ Behavioural round — STAR method evaluation against leadership principles that product companies score on

→ Resume walkthrough — how you present your projects and whether your story signals the right seniority level

After the session:

→ Detailed verbal feedback on every section — strengths, gaps, and exactly why each answer did or didn't land

→ Honest readiness assessment for your target role and level

→ Specific 2-week prep plan based on your actual gaps

If you're not ready, you'll know before the real interview — not after.

Ideal for: DS professionals with 2–7 years of experience targeting Senior DS or MLE roles at product companies.