Data Science Interview Questions in Mumbai

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It checks whether observed category counts differ from expected counts, used for independence between two categorical variables and for goodness of fit.

Your SQL result does not match the number on the official dashboard. How do you resolve it?

SQL for Data ScienceMedium

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I would first align definitions: date range, time zone, filters like test users or cancelled orders, and whether the metric counts users or events. Then compare at a smaller grain, by day or by city, to find where the gap starts. I would read the dashboard's underlying query and check data refresh times. Once found, I would document the agreed definition so the same mismatch does not repeat.

What is chunking in RAG and how do you choose a chunk size?

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Chunking splits documents into smaller pieces before embedding them, so retrieval returns focused passages that fit in the prompt. Small chunks, like 200 tokens, are precise but lose context; large chunks, like 1,000 tokens, keep context but add noise and cost. Split on natural boundaries such as headings and paragraphs, add some overlap, and keep metadata like title and section. Then tune chunk size using retrieval metrics on real questions.

Tell me about a GenAI project you built and what you learned from it.

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Situation: our HR team answered the same leave and policy questions daily. Task: build an internal assistant in six weeks. Action: I built a RAG pipeline over policy PDFs, added citations, created a 150-question evaluation set with HR, and iterated on chunking and reranking. Result: answer accuracy reached 91 percent and HR queries dropped by 40 percent. I learned that the evaluation set mattered more than the choice of model.

What is the randomisation unit in an experiment, and why does it matter?

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The randomisation unit is what you assign to variants: a user, a session, a device, a page view or even a city. Choosing user-level keeps each person's experience consistent and avoids contamination. The analysis unit should match the randomisation unit, because if you randomise by user but analyse by page view, observations are correlated and standard errors come out too small, producing false positives. Use the delta method or bootstrap when they differ.

Explain logistic regression.

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It models the log-odds of a binary outcome as a linear function and passes it through a sigmoid to give probabilities, trained by maximising likelihood.

How do you deal with high-cardinality categorical features?

Feature EngineeringMedium

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Use target or frequency encoding with cross-validation, hashing, embeddings or grouping rare categories, instead of huge one-hot matrices.

The business asks you to pick a decision threshold for a churn model. How do you choose it?

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The default 0.5 is rarely right. I would get the cost of each outcome: the value of saving a churner, the cost of a retention offer, and the team's capacity to contact customers. Then I compute expected profit or precision and recall at many thresholds on validation data and pick the one that maximises value within capacity. I would also check that the chosen threshold stays stable over recent months.

How does a CNN work?

Deep Learning and NLPMedium

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Convolution filters slide over the input to detect local patterns, pooling reduces size, and deeper layers combine edges into shapes and objects. Used mainly for images.

What is a confidence interval?

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A range built from sample data that would contain the true parameter in a stated percentage (for example 95%) of repeated samples. It is not the probability that the parameter lies inside this one interval.

Explain the KNN algorithm and its drawbacks.

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It predicts from the k nearest labelled points. It is simple but slow at prediction time, sensitive to scaling and irrelevant features, and weak in high dimensions.

Tell me about a time you had to build a model when the requirements were unclear.

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Situation: a sales head asked for a model to find good leads but could not define good. Task: turn this into a clear target. Action: I met the team, looked at past deals, and proposed converted within 60 days as the label, with expected revenue as a secondary view. I shared a quick baseline in a week to get feedback. Result: the definition was agreed, and the final model raised conversion in the top decile.

What is the difference between parametric and non-parametric tests?

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Parametric tests assume a distribution (usually normal) and use means; non-parametric tests such as Mann-Whitney use ranks and make fewer assumptions.

Explain the Law of Large Numbers.

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As the number of independent trials grows, the sample average converges to the true expected value, which is why larger samples give more stable estimates.

What is an isolation forest and how does it detect anomalies?

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An isolation forest builds many random trees that split data on random features at random thresholds. Anomalies are rare and different, so they get isolated in very few splits, giving a short average path length. Normal points need many splits. The anomaly score comes from that path length. It is fast, works without labels and scales to large data, but you must choose a contamination rate or score threshold carefully.

A deep network you built from scratch does not learn at all; the loss stays flat from the first epoch. Could weight initialisation be the reason?

Deep Learning and NLPMedium

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If weights start too large, activations and gradients explode; if too small, they vanish, and training stalls. If all weights are equal, every neuron learns the same thing. Good schemes keep the variance of activations stable across layers: Xavier or Glorot initialisation for tanh and sigmoid, and He initialisation for ReLU. Frameworks use sensible defaults, but it matters when building custom layers or very deep networks.

How do you handle missing values?

Python for Data ScienceEasy

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First understand why data is missing. Then drop rows or columns with too many nulls, impute with mean/median/mode or a model (KNN), add a 'was_missing' flag, and fit imputers on training data only.

How would you explain a statistical concept to a non-technical manager?

Statistics and ProbabilityEasy

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I start with the decision they care about, not the formula. Once a sales manager asked why a 3 percent lift was not significant. I explained that with only 200 customers, the result could easily happen by chance, like getting 6 heads in 10 coin tosses. I showed a simple range chart of the confidence interval and suggested running the test longer. He agreed, and the extended test gave a clear answer.

How do you turn text columns into features for a tabular model?

Feature EngineeringMedium

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Simple options are length, word count and keyword flags. TF-IDF vectors capture important words and work well with linear models. For meaning, use sentence embeddings from a pretrained model and optionally reduce them with PCA. You can also train a separate text classifier and feed its predicted score as one feature. Fit any vocabulary or encoder on training data only to avoid leakage.

What is non-maximum suppression in object detection?

Computer VisionEasy

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A detector usually predicts many overlapping boxes for the same object. Non-maximum suppression keeps the box with the highest confidence, removes other boxes of the same class whose IoU with it exceeds a threshold such as 0.5, and repeats with the remaining boxes. A threshold that is too low can delete genuinely nearby objects in crowded scenes, so variants like soft-NMS lower scores instead of removing boxes.

Tree feature importance ranks an ID-like numeric column near the top. Why, and which importance method would you trust instead?

Feature EngineeringHard

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Impurity-based importance, the default in tree models, sums how much each feature reduces impurity across splits. It is fast but biased toward high-cardinality and continuous features, and it is computed on training data. Permutation importance shuffles one feature at a time on validation data and measures the drop in score, so it reflects real predictive value. Both can understate correlated features, because the model can lean on the copy.

What is an AI agent in the context of LLMs?

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An AI agent is an LLM that works in a loop: it plans a step, calls a tool, reads the result and decides the next step until the goal is met. For example, it might query a database, run Python on the result and write a summary. Agents handle multi-step tasks but are less predictable, slower and costlier than a single call. Good designs limit tools and steps, log every action and keep a human check for risky actions.

Write Python code to fit a Holt-Winters model and forecast the next 12 months.

Time Series & ForecastingMedium

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from statsmodels.tsa.holtwinters import ExponentialSmoothing s = df.set_index('month')['sales'].asfreq('MS') train, test = s[:-12], s[-12:] model = ExponentialSmoothing( train, trend='add', seasonal='mul', seasonal_periods=12 ).fit() forecast = model.forecast(12) mae = (forecast - test).abs().mean() print(forecast.round(1), 'MAE:', round(mae, 1))

Your deployed model gives different predictions than the same model in your notebook. Why?

MLOps and Model DeploymentMedium

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Usually preprocessing differs between training and serving. Check that the same pipeline object was saved, not just the estimator, and that category order, missing value handling and scaling match. Compare library versions, feature types like strings versus integers, column order, and time zones in date features. Send the same few rows through both paths and compare features step by step to find the first mismatch.

A feature won its A/B test with a 4 percent lift, but after full launch the metric barely moved. Why might that happen?

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Several reasons are common. Winner's curse: significant results from underpowered tests tend to overestimate the true effect. Novelty effect faded after launch. Seasonality or other launches changed the baseline, so a before-after comparison is misleading. The test population may differ from all users, for example only one platform. Use a holdout after launch to measure real impact, and run tests with adequate power and duration.

What is a p-value?

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The probability of seeing a result at least as extreme as the observed one if the null hypothesis were true. A small p-value is evidence against the null, not proof that the alternative is true.

What is effect size and why should you report it along with a p-value?

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Effect size measures how large a difference is, for example the absolute lift in conversion or Cohen's d for means. A p-value only says whether the difference is likely due to chance. With millions of users, a 0.01 percent lift can be significant yet useless for the business. Reporting effect size with a confidence interval tells decision makers both whether the effect is real and whether it is worth acting on.

What is quantisation of LLMs?

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Quantisation stores model weights in lower precision, such as 8-bit or 4-bit integers instead of 16-bit floats. A 7-billion parameter model drops from about 14 GB to around 4 GB at 4-bit, so it fits on smaller GPUs or even laptops and runs faster. The cost is some loss in quality, usually small at 8-bit and noticeable on hard tasks at very low bits. Common formats include GPTQ, AWQ and GGUF.

Your power analysis says you need 200,000 users per group, but the page gets only 5,000 users a week. What are your options?

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Waiting 80 weeks is unrealistic. Options: test a bolder change so the expected effect is larger; use a more sensitive metric closer to the change, like add-to-cart instead of purchase; apply CUPED to reduce variance; or move the test to a higher-traffic page. If none work, accept a larger MDE explicitly, or decide using qualitative research and a staged rollout with monitoring instead of a formal test.

What is the Global Interpreter Lock (GIL)?

Python for Data ScienceMedium

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A mutex in CPython that lets only one thread execute Python bytecode at a time. Use multiprocessing for CPU-bound work and threads or asyncio for I/O-bound work.

Tell me about a time an experiment result changed a product decision.