Data Science Interview Questions in India (2026): What They Actually Ask

Most "data science interview questions" lists are 200 disconnected trivia items you'll forget by morning. That's not how interviews are won. Data science interviews in India cluster into a handful of predictable *rounds*, each testing a different thing. Prepare by round, understand what each is really probing, and you'll walk in calm. Here's the map, from someone who has been on both sides of the table.

Round 1 — SQL

Almost every data interview has a SQL round, and it's where a lot of candidates quietly fail. Expect to write live queries: aggregations with GROUP BY/HAVING, multi-table JOINs, and — the classic filter — a window-function question like "find the top 3 products by revenue in each category" or "month-over-month growth." They're testing whether you've actually queried real data. Prepare with SQL for Data Science.

Round 2 — Python / coding

Usually pandas manipulation ("clean and reshape this dataframe") and some light algorithmic thinking — not hard LeetCode, but comfort with logic, strings and dictionaries. Increasingly they'll ask how you'd use an AI copilot responsibly, and expect you to explain code, not just produce it.

Round 3 — Statistics & ML fundamentals

Conceptual, and where shallow prep shows. Common themes:

• Bias–variance tradeoff; overfitting — what it is and how you'd detect and fix it.
• Evaluation metrics — precision vs recall, when accuracy misleads, ROC-AUC, and choosing a metric that matches the business.
• How specific models work — explain a random forest or gradient boosting simply; when you'd pick one over logistic regression.
• Handling imbalanced data, missing values, and feature leakage — practical judgement questions.
• Correlation vs causation — a favourite, because it reveals whether you think carefully.

Round 4 — the case / business round

The round that most separates candidates. You're given an open problem — "our users are churning, how would you approach it?" — and assessed on how you *think*: do you clarify the goal, frame it as a measurable problem, reason about what data you'd need, choose a sensible approach, and think about how the result would be used? There's rarely one right answer; they're watching your structure. This is the hardest thing to cram and the easiest to build through real projects.

Round 5 — your projects (the one you control)

They'll dig into something on your resume. This is where a portfolio of deployed projects wins, because you can speak to real decisions and trade-offs instead of reciting a tutorial. Expect "why did you choose that model?", "what would you do differently?", "how did you evaluate it?" Have strong projects you can defend end to end.

Interviewers can tell in two minutes whether you built something real or followed a tutorial. Build real things, then you don't have to bluff.

The modern-AI questions (new for 2026)

Increasingly common: "When would you use an LLM versus a traditional model?", "What is RAG and when is it appropriate?", "How would you evaluate an LLM's output?" Basic fluency here now differentiates candidates. If those terms are unfamiliar, start with What is RAG.

How to prepare (in priority order)

1. 1Get genuinely fluent in SQL — highest ROI, most reliably tested.
2. 2Build and be able to defend 3–5 real projects, ideally deployed.
3. 3Drill ML and stats fundamentals conceptually, out loud, in plain English.
4. 4Practise case questions by talking through open problems, structure first.
5. 5Pick up basic modern-AI fluency so the 2026 questions don't surprise you.

The bottom line

Data science interviews aren't a trivia test — they're five predictable rounds probing SQL, coding, fundamentals, business thinking and your real work. Prepare by round rather than by memorising lists, lean hardest on SQL and defensible projects, and add a layer of modern-AI fluency for 2026. Structure beats cramming every time.