Data Science Interview Questions for Indian Companies 2026

The skill mix in Indian DS interviews depends heavily on role type and company stage.

Product analytics DS (Swiggy, Zomato, CRED, Meesho): heavy on SQL, A/B testing, and product metric interpretation. ML experience is nice-to-have, not required. ML engineering DS (Flipkart, Amazon, PhonePe): end-to-end ML pipeline: feature engineering, model selection, productionisation, model monitoring. Research DS (Microsoft Research India, Google DeepMind India, IBM Research): novel algorithm design, paper-level depth, typically requires a strong MS/PhD background. The mistake most Indian candidates make: preparing for an ML engineering role when applying for a product analytics role, or vice versa. Read the job description carefully for which skills are prioritised.

Statistics is the foundation tested in almost every Indian DS interview.

A/B testing: how to design a valid test (hypothesis, control/treatment groups, sample size calculation, runtime, p-value interpretation). Common question: 'Our A/B test shows p=0.04: do we ship the change?': the answer is not just yes; discuss practical significance, the multiple testing problem, and Bonferroni correction. Probability: conditional probability (Bayes' theorem), birthday problem, unfair coin: these appear in Flipkart and Amazon DS interviews. Distributions: normal, Poisson, binomial: know when each applies. Common question: 'If we see 10 customer support tickets per hour on average, what is the probability of seeing more than 15 in a given hour?': Poisson distribution.

ML questions vary widely by role: this covers what most Indian product company DS interviews test.

Bias-variance trade-off: high bias = underfitting (too simple model); high variance = overfitting (too complex model). Regularisation (L1/L2) as the lever. Cross-validation: k-fold cross-validation purpose and mechanics. Why use it instead of a single train/test split. Classification metrics: precision, recall, F1, AUC-ROC: when does recall matter more than precision? (fraud detection, medical diagnosis). Gradient Boosting: the dominant algorithm in Indian DS interviews: how XGBoost/LightGBM works conceptually, why it outperforms random forests in practice. Feature engineering: handling missing values (imputation strategies), categorical encoding (one-hot vs label vs target encoding), feature scaling (standardisation vs normalisation).

SQL is consistently the most tested technical skill in Indian data science interviews.

Expected SQL depth: window functions (ROW_NUMBER, RANK, LAG/LEAD), CTEs for complex queries, self-joins, handling NULLs in aggregations. Common DS SQL question: 'Find the top 3 products by revenue in each category, and show month-over-month revenue change.' Python for DS: Pandas (groupby, merge, pivot, handling missing data), NumPy (vectorised operations), Matplotlib/Seaborn for quick visualisations during take-home assignments. Scikit-learn: model training, cross-validation, GridSearchCV: you should be able to write a complete model pipeline (load data → feature engineering → train/CV → evaluate) from memory.

Product intuition questions are the differentiator between good and great DS candidates in Indian product companies.

Common format: 'Swiggy wants to reduce food delivery time: how would you approach this as a data scientist?' Expected answer structure: (1) Clarify what 'delivery time' means and what success looks like (metric definition). (2) Decompose into components (restaurant prep time, pickup wait, delivery route). (3) Identify data sources for each component. (4) Propose experiments or ML models for the highest-leverage component. (5) Discuss how to measure impact and handle edge cases. Business metric design: 'We want to improve user retention: what metrics would you track and what experiments would you run?': this tests whether you can think from first principles rather than just applying ML models.