Data Science Interview Questions in Bangalore

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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.

Why is cross-entropy preferred over MSE for classification in neural networks?

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Cross-entropy measures how far predicted probabilities are from the true class and strongly penalises confident wrong predictions. With a softmax or sigmoid output, it gives large, useful gradients when the model is wrong. MSE with sigmoid outputs gives very small gradients when predictions saturate, so learning slows down. Use binary cross-entropy for two classes, categorical cross-entropy for many classes, and MSE for regression targets.

Write Python code to run an ADF stationarity test and difference the series if needed.

Time Series & ForecastingMedium

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from statsmodels.tsa.stattools import adfuller def is_stationary(s, alpha=0.05): p_value = adfuller(s.dropna())[1] return p_value < alpha series = s.copy() d = 0 while not is_stationary(series) and d < 2: series = series.diff() d += 1 print('differencing order d =', d)

Explain Bayes' theorem with an example.

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P(A given B) = P(B given A) x P(A) / P(B). In a medical test it converts test accuracy and disease prevalence into the real chance a positive result means disease.

What is the difference between bagging and boosting?

Machine LearningEasy

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Bagging trains models in parallel on bootstrap samples and averages them (reduces variance, e.g. Random Forest). Boosting trains models one after another, each fixing the previous errors (reduces bias, e.g. XGBoost).

What is transfer learning?

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Reusing a model pretrained on a large dataset and fine-tuning it on a smaller task-specific dataset, saving data, time and compute.

Write Python code to group a list of order dictionaries by city and sum the amounts without Pandas.

Python for Data ScienceEasy

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from collections import defaultdict orders = [ {'city': 'Pune', 'amount': 250}, {'city': 'Delhi', 'amount': 400}, {'city': 'Pune', 'amount': 150}, ] totals = defaultdict(float) for o in orders: totals[o['city']] += o['amount'] print(dict(totals)) # {'Pune': 400.0, 'Delhi': 400.0}

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 stationarity and why do many forecasting models need it?

Time Series & ForecastingEasy

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A stationary series has a constant mean, constant variance and an autocorrelation structure that does not change over time. Classical models like ARMA assume stationarity because they learn fixed relationships between past and present values; if the mean keeps drifting, those relationships break. You usually make a series stationary by differencing to remove trend, seasonal differencing to remove seasonality, and a log transform to stabilise growing variance.

How does a CNN work?

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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.

Write Python code that validates prediction requests with Pydantic before passing them to a model.

MLOps and Model DeploymentMedium

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from pydantic import BaseModel, Field, ValidationError class LoanRequest(BaseModel): age: int = Field(ge=18, le=100) income: float = Field(gt=0) city: str = Field(min_length=2) def predict(payload: dict): try: req = LoanRequest(**payload) except ValidationError as e: return {'error': e.errors()} return {'score': float(model.predict_proba([[req.age, req.income]])[0, 1])}

How do you tune hyperparameters?

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Use grid search, random search or Bayesian optimisation with cross-validation, starting with a coarse random search and narrowing the promising ranges.

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))

What is the bias–variance trade-off?

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Bias is error from overly simple assumptions (underfitting). Variance is sensitivity to the training data (overfitting). More model complexity lowers bias but raises variance — aim for the lowest total error using cross-validation and regularization.

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.

Your LLM gives different summaries for the same document on different runs, but auditors need consistent output. What do you do?

Generative AI and LLMsMedium

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Set temperature to zero or near zero and fix other sampling settings, and use a seed where the API supports it, though outputs may still vary slightly. Pin the exact model version. Use a structured output format with fixed fields, and reduce ambiguity in the prompt. Cache and store each generated output with its inputs and version, so the stored result, not a regeneration, is what auditors see.

Tell me about a time your analysis contradicted what leadership expected.

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Situation: leadership believed a discount campaign drove a sales spike. Task: I was asked to measure its impact. Action: I compared similar regions with and without the campaign and found the spike matched a festival week in both. I shared the method, the charts and the limits openly. Result: they cut the next discount budget by half and moved it to retention. I learned to present surprising results with clear evidence and calm framing.

Write SQL to show each customer's first order date, last order date and the gap between them in days.

SQL for Data ScienceMedium

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SELECT customer_id, MIN(order_date) AS first_order, MAX(order_date) AS last_order, MAX(order_date) - MIN(order_date) AS days_between, COUNT(*) AS orders FROM orders GROUP BY customer_id ORDER BY days_between DESC; -- PostgreSQL: DATE minus DATE gives days; use DATEDIFF in other engines

What is the difference between == and is?

Python for Data ScienceEasy

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== compares values for equality, while is checks whether two names point to the same object in memory. Use is mainly for None checks.

How do you choose a learning rate for a neural network?

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Start with a sensible default such as 1e-3 for Adam, run a learning-rate range test, and use schedulers or warm-up while watching the loss curve.

Write Pandas code to create lag and rolling features for each customer.

Feature EngineeringMedium

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df = df.sort_values(['customer_id', 'month']) g = df.groupby('customer_id')['spend'] df['spend_lag1'] = g.shift(1) df['spend_lag3'] = g.shift(3) # shift first so the current month is not included df['spend_roll3_mean'] = g.transform(lambda s: s.shift(1).rolling(3).mean())

Your neural network's training loss suddenly becomes NaN. What do you check?

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Common causes are a learning rate that is too high, exploding gradients, bad input values, and unstable maths like log of zero. I would check the input data for NaN or infinite values, lower the learning rate, add gradient clipping, and make sure the loss uses the stable built-in version with logits. With mixed precision, I would check the loss scaler. Logging gradient norms per step helps find the exact point it breaks.

A feature works well offline but is not available at prediction time in production. How do you handle this?

Feature EngineeringHard

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This is training-serving skew. The feature might be computed from data that arrives later, like final order status. First confirm exactly when each feature is available in production. Rebuild the training set using point-in-time values, as they were at prediction time, or drop the feature. A feature store with point-in-time joins and the same feature code for training and serving prevents this. Expect offline scores to drop, but they become honest.

What is backpropagation?

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It computes the gradient of the loss with respect to every weight by applying the chain rule backwards through the network, so gradient descent can update them.

What is the receptive field of a neuron in a CNN?

Computer VisionMedium

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The receptive field is the region of the input image that can influence one activation. Early layers see small patches like edges, and deeper layers see larger regions, enabling recognition of whole objects. It grows with kernel size, depth, stride and pooling, and dilated convolutions increase it without extra parameters. If the receptive field is smaller than the objects you care about, the network cannot capture their full context.

Type I vs Type II error?

Statistics and ProbabilityEasy

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Type I = false positive (rejecting a true null), probability α. Type II = false negative (missing a real effect), probability β. Power = 1 − β.

What is calibration of a classifier?

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Calibration checks whether predicted probabilities match real frequencies (e.g. 70% predictions are right about 70% of the time). Fix with Platt scaling or isotonic regression.

Soon after a new model version went live, customer complaints spiked. How do you roll back safely and learn from it?

MLOps and Model DeploymentMedium

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Roll back immediately to the previous registry version through the deployment pipeline, rather than editing servers by hand, and confirm metrics recover. Then investigate offline: compare predictions of both versions on the same traffic, check feature pipelines and data changes, and review what the offline evaluation missed. Add the failing cases to the test set, and use canary or shadow rollout with alert thresholds for future releases.

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.

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.

Tell me about a time you had to explain a deep learning model's mistake to a client or stakeholder.

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Situation: our document classifier mislabelled several legal notices, and the client was upset. Task: explain clearly and restore confidence. Action: I showed the misclassified examples, explained that they used a format absent from training data, and avoided jargon. I proposed adding those samples, a confidence threshold for human review and weekly error reports. Result: errors on that format fell sharply and the client extended the project.

Your LLM assistant confidently quotes a policy clause that does not exist. Why does this happen and how do you reduce it?

Generative AI and LLMsMedium

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Hallucination is when a model produces confident text that is false or not supported by any source. It happens because the model generates plausible tokens rather than checking facts. Reduce it by grounding answers in retrieved documents, asking the model to cite sources and say I do not know when context is missing, using low temperature, and validating outputs with rules or a second checking step. Measure it with a labelled evaluation set.

What is Bayesian A/B testing and how are its results reported?

A/B Testing & ExperimentationMedium

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Bayesian A/B testing starts with a prior on each variant's metric, updates it with observed data and produces a posterior distribution. Instead of a p-value, you report things like the probability that treatment beats control, say 94 percent, and the expected loss if you choose the wrong variant. Business teams find this easier to interpret. Priors must be chosen sensibly, and continuous monitoring still needs discipline about decision rules.

Tell me about a time your forecast was badly wrong. How did you handle it?

Time Series & ForecastingMedium

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Situation: my weekly demand forecast under-predicted a category by 30 percent for a month. Task: explain the miss and stop it recurring. Action: I compared forecast and actuals by region and found a new distribution channel had launched that the model knew nothing about. I told the planning team quickly, added channel as a feature and set up a weekly error review. Result: error returned to normal and planners trusted the alerts.

You added an index but the query is still slow. How do indexes work, and when do they fail to help or even hurt?

SQL for Data ScienceMedium

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An index is a separate sorted structure, usually a B-tree, that lets the database find rows without scanning the whole table, like a book index. It speeds up filters, joins and sorting on the indexed columns. Indexes cost storage and slow down inserts, updates and deletes because each index must be updated. They may be ignored for low-selectivity columns or when a function is applied to the column in WHERE.

What is sampling bias and how can you reduce it?