Data Scientist Interview Prep & Rubrics

Modelling and inference rather than platform: supervised methods, features and leakage, metric choice, experimentation and statistics, and defending a result to a stakeholder.

The loop, in order

1. 1Machine Learning24 questions · 8 topicsThe modelling half of an ML loop: supervised and unsupervised methods, features and leakage, metric choice, imbalance, deep learning foundations, forecasting and ranking.
2. 2Data & AI Engineering40 questions · 8 topicsData engineering, ML engineering and LLM/RAG interview questions covering pipelines, model evaluation, overfitting, retrieval, embeddings, vector search and production AI systems.
3. 3Python22 questions · 7 topicsPython for backend, automation, and data roles, including the runtime behaviour that surprises candidates who only know the syntax.
4. 4Databases & SQL28 questions · 8 topicsQuery-writing rounds and the storage-engine reasoning behind why a query is slow, for backend, data, and DBA roles alike.
5. 5Product Management22 questions · 7 topicsThe PM loop as it is actually run: product sense, analytical, execution, and strategy rounds, each with its own scoring rubric.
6. 6Behavioural & Culture Fit25 questions · 8 topicsThe round candidates most often under-prepare, treated as a skill with structure rather than small talk, including the rubric interviewers score against.
7. 7HR, Screening & Offer19 questions · 6 topicsThe recruiter and HR rounds that gate the technical loop and decide your compensation, where most candidates improvise and lose money.