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Data Science Interview Questions in Noida

36 practice questions with answers for Data Science interviews in Noida.

Data Science jobs in Noida

Noida is a major IT and IT-services hub in Delhi NCR with large campuses of service companies, product firms and fast-growing start-ups along the Noida–Greater Noida Expressway. Freshers often find their first role here, and many teams run hybrid work.

Below are 36 questions that match the mix of service, product and domain employers hiring data scientists in Noida.

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Your notebook runs on your laptop but fails on a colleague's machine. How do you make the work reproducible?

Python for Data ScienceMedium

Show answer

First pin the environment: a requirements.txt or conda environment file with exact versions, or a Docker image. Set random seeds for NumPy, scikit-learn and any framework you use. Remove hard-coded local paths and read them from a config file. Make sure data comes from a shared, versioned location. Finally, restart the kernel and run the notebook top to bottom, so hidden cell order bugs are caught before sharing.

What should you log for a production prediction service?

MLOps and Model DeploymentMedium

Show answer

For each request, log a request ID, timestamp, model version, input features or a hash of them, the prediction and score, and latency. Log errors and fallback use separately. Do not log raw personal data unless needed and allowed; mask it. Aggregate these logs into dashboards for traffic, latency percentiles, error rate and score distribution. Joining logged predictions with later outcomes gives the data for monitoring and retraining.

Write SQL to compute a 7-day rolling average of daily active users.

SQL for Data ScienceMedium

Show answer

WITH daily AS ( SELECT activity_date, COUNT(DISTINCT user_id) AS dau FROM events GROUP BY activity_date ) SELECT activity_date, dau, AVG(dau) OVER (ORDER BY activity_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS dau_7d_avg FROM daily ORDER BY activity_date;

What is the difference between a base model and an instruction-tuned model?

Generative AI and LLMsEasy

Show answer

A base model is trained only to predict the next token on large text, so it continues text rather than following requests. An instruction-tuned model is further trained on instruction and answer pairs, and often preference data, so it follows prompts, answers questions and behaves like an assistant. For apps you usually use instruction-tuned or chat models. Base models are mainly starting points for custom fine-tuning.

Explain ROC curve and AUC.

Machine LearningMedium

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The ROC curve plots true positive rate against false positive rate across thresholds. AUC is the probability that a random positive is ranked above a random negative; 0.5 is chance.

What are common ways to evaluate NLP models?

Deep Learning and NLPMedium

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Accuracy, F1 and confusion matrices for classification, BLEU or ROUGE for generation and summarisation, perplexity for language models, plus human review.

What is transfer learning?

Deep Learning and NLPMedium

Show answer

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

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

Generative AI and LLMsMedium

Show answer

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 are context managers in Python?

Python for Data ScienceEasy

Show answer

A context manager sets something up and guarantees cleanup when a block ends, even if an error is raised. You use it with the with statement, for example with open('data.csv') as f, which always closes the file. It works through __enter__ and __exit__ methods, or the contextlib.contextmanager decorator. In data work it is used for files, database connections, locks and timers, so resources are never left open.

What is the minimum detectable effect (MDE) in experiment design?

A/B Testing & ExperimentationMedium

Show answer

The MDE is the smallest true effect your test can reliably detect given the sample size, baseline rate, significance level and power. If baseline conversion is 5 percent and your MDE is 10 percent relative, you can detect a move to 5.5 percent but will likely miss smaller lifts. Choose the MDE from business value: the smallest change worth shipping. A smaller MDE needs a much larger sample, roughly growing with one over MDE squared.

Write Python code to compare revenue per user between two groups using Welch's t-test.

A/B Testing & ExperimentationMedium

Show answer

import numpy as np from scipy import stats ctrl = df.loc[df.variant == 'control', 'revenue'].to_numpy() treat = df.loc[df.variant == 'treatment', 'revenue'].to_numpy() t, p = stats.ttest_ind(treat, ctrl, equal_var=False) # Welch diff = treat.mean() - ctrl.mean() se = np.sqrt(treat.var(ddof=1) / len(treat) + ctrl.var(ddof=1) / len(ctrl)) print(f'diff={diff:.2f} 95% CI=({diff - 1.96*se:.2f}, {diff + 1.96*se:.2f}) p={p:.4f}')

What is MAPE and when is it a poor metric for forecast accuracy?

Time Series & ForecastingMedium

Show answer

MAPE is the mean of absolute errors divided by actual values, expressed as a percentage. It is easy to explain to business teams, but it breaks when actuals are zero or near zero, because the percentage explodes. It also penalises over-forecasts more than under-forecasts and weights small items heavily. For intermittent or low-volume series prefer MAE, weighted MAPE or MASE, which compares error to a naive baseline.

Tell me about a computer vision project where data quality was the biggest challenge.

Computer VisionMedium

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Situation: a shelf-detection model plateaued at low accuracy. Task: find out why and fix it. Action: I reviewed 500 errors and found that a third were labelling mistakes, with inconsistent boxes for partly hidden products. I rewrote the guidelines with visual examples, had a second annotator review hard images and relabelled the worst batch. Result: mAP rose by 9 points without changing the model, and labelling rework dropped sharply.

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.

Write SQL to compute a running total of spend for each user.

SQL for Data ScienceMedium

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SELECT user_id, order_date, amount, SUM(amount) OVER ( PARTITION BY user_id ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW ) AS running_spend FROM orders ORDER BY user_id, order_date;

How does K-means clustering work and how do you choose k?

Machine LearningMedium

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It assigns points to the nearest centroid and updates centroids until stable. Choose k with the elbow method, silhouette score or business context.

Write SQL to compute funnel conversion from visit to cart to purchase.

SQL for Data ScienceHard

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WITH steps AS ( SELECT user_id, MAX(CASE WHEN event = 'visit' THEN 1 ELSE 0 END) AS visited, MAX(CASE WHEN event = 'add_to_cart' THEN 1 ELSE 0 END) AS carted, MAX(CASE WHEN event = 'purchase' THEN 1 ELSE 0 END) AS purchased FROM events WHERE event_date >= '2026-10-01' GROUP BY user_id ) SELECT SUM(visited) AS visits, SUM(carted) AS carts, SUM(purchased) AS buyers, ROUND(100.0 * SUM(carted) / NULLIF(SUM(visited), 0), 1) AS visit_to_cart_pct, ROUND(100.0 * SUM(purchased) / NULLIF(SUM(carted), 0), 1) AS cart_to_buy_pct FROM steps;

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

Show answer

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.

How do you evaluate and explain a model to business stakeholders?

MLOps and Model DeploymentHard

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Tie metrics to business outcomes such as cost saved, show feature importance or SHAP values, give example predictions and be transparent about limitations.

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.

What is TF-IDF?

Deep Learning and NLPMedium

Show answer

Term Frequency × Inverse Document Frequency: a word scores high if it is frequent in one document but rare across all documents. A classic way to turn text into numeric features.

Most of your products sell zero units on most days, with occasional sales. Which forecasting approach suits this?

Time Series & ForecastingMedium

Show answer

This is intermittent demand, and standard models like ARIMA forecast a small constant that is useless for stocking. Croston's method and its SBA variant forecast the size of non-zero demand and the interval between sales separately. Alternatives are aggregating to weekly level, or using a global gradient boosting model with Tweedie or Poisson loss. Evaluate with MAE or MASE, not MAPE, because zero actuals break percentage errors.

Pandas: compute a 7-day rolling average of daily sales for each store.

Python for Data ScienceMedium

Show answer

# df has columns: store, date, sales df['date'] = pd.to_datetime(df['date']) df = df.sort_values(['store', 'date']) df['sales_7d_avg'] = ( df.groupby('store')['sales'] .transform(lambda s: s.rolling(7, min_periods=1).mean()) )

Write Pandas code to cap outliers at the 1st and 99th percentile.

Feature EngineeringEasy

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# fit limits on training data only, then apply to test low, high = train['income'].quantile([0.01, 0.99]) train['income_capped'] = train['income'].clip(low, high) test['income_capped'] = test['income'].clip(low, high)

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 RAG chatbot often answers using the wrong documents. How do you fix it?

Generative AI and LLMsHard

Show answer

First check retrieval on its own with a labelled set of questions, since generation cannot fix bad context. Then improve chunking so passages are self-contained, add metadata filters such as product or date, try a better embedding model, and add hybrid search with keywords. A reranker on the top 20 results often helps most. Also rewrite vague user questions into clearer search queries before retrieval.

Explain the chi-square test.

Statistics and ProbabilityMedium

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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.

What is U-Net and why is it popular for segmentation?

Computer VisionMedium

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U-Net is an encoder-decoder network. The encoder downsamples to capture context, and the decoder upsamples back to full resolution to produce a per-pixel mask. Skip connections copy encoder feature maps to the matching decoder level, so fine spatial detail lost during downsampling is recovered. It works well with limited labelled data, which made it the standard for medical imaging and industrial defect segmentation, and it is also the backbone of many diffusion models.

Explain INNER, LEFT, RIGHT and FULL OUTER joins.

SQL for Data ScienceEasy

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An INNER JOIN returns only rows with matching keys in both tables. A LEFT JOIN returns all rows from the left table, with NULLs where the right has no match. A RIGHT JOIN is the mirror of that. A FULL OUTER JOIN returns all rows from both sides, matched where possible. In analytics, LEFT JOIN from a customers table is common so customers with no orders are still counted.

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

Statistics and ProbabilityMedium

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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 statistical power and how can you increase it?

Statistics and ProbabilityMedium

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Power is the probability of detecting a real effect. Increase it with a larger sample, a bigger effect size, lower noise or a higher significance level.

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

A/B Testing & ExperimentationMedium

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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.

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

Generative AI and LLMsMedium

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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.

A feature was launched to all users with no control group. How would you estimate its causal impact?

Statistics and ProbabilityHard

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Without randomisation I would use quasi-experimental methods. Difference-in-differences compares the change in a group that got the feature early with one that did not, if rollout was staggered. Interrupted time series or synthetic control builds a counterfactual from pre-launch trends or similar markets. Regression discontinuity works if eligibility had a cut-off. I would state the assumptions clearly and check them with placebo tests.

What is SHAP and how does it explain model predictions?

Machine LearningHard

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SHAP assigns each feature a contribution to a single prediction based on Shapley values from game theory. The contributions add up to the difference between the prediction and the average prediction. It gives local explanations, like why one loan was rejected, and global views by aggregating values across rows. TreeSHAP is fast for tree models. Limits include slow computation for other models and misleading results when features are strongly correlated.

Tell me about a time you had to learn a new deep learning framework or tool quickly.

Deep Learning and NLPMedium

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Situation: my internship project used PyTorch, but I had only used Keras. Task: build a text classifier within two weeks. Action: I followed the official tutorials for two days, rebuilt a small Keras model in PyTorch to compare, and asked a senior to review my training loop. Result: I delivered the model on time with 88 percent F1. I learned that rebuilding something familiar is the fastest way into a new tool.

Companies with offices in Noida (22)

Product

Microsoft

Data Science interview questions

40 questions
5 rounds

Hyderabad, Bengaluru, Noida +2

Data Science Interview Questions in Noida

Data Science jobs in Noida

Your notebook runs on your laptop but fails on a colleague's machine. How do you make the work reproducible?

What should you log for a production prediction service?

Write SQL to compute a 7-day rolling average of daily active users.

What is the difference between a base model and an instruction-tuned model?

Explain ROC curve and AUC.

What are common ways to evaluate NLP models?

What is transfer learning?

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

What are context managers in Python?

What is the minimum detectable effect (MDE) in experiment design?

Write Python code to compare revenue per user between two groups using Welch's t-test.

What is MAPE and when is it a poor metric for forecast accuracy?

Tell me about a computer vision project where data quality was the biggest challenge.

What is stationarity and why do many forecasting models need it?

Write SQL to compute a running total of spend for each user.

How does K-means clustering work and how do you choose k?

Write SQL to compute funnel conversion from visit to cart to purchase.

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

How do you evaluate and explain a model to business stakeholders?

What is the Global Interpreter Lock (GIL)?

What is TF-IDF?

Most of your products sell zero units on most days, with occasional sales. Which forecasting approach suits this?

Pandas: compute a 7-day rolling average of daily sales for each store.

Write Pandas code to cap outliers at the 1st and 99th percentile.

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

Your RAG chatbot often answers using the wrong documents. How do you fix it?

Explain the chi-square test.

What is U-Net and why is it popular for segmentation?

Explain INNER, LEFT, RIGHT and FULL OUTER joins.

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

What is statistical power and how can you increase it?

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

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

A feature was launched to all users with no control group. How would you estimate its causal impact?

What is SHAP and how does it explain model predictions?

Tell me about a time you had to learn a new deep learning framework or tool quickly.

Companies with offices in Noida (22)

Microsoft

Adobe

Oracle

Paytm

Barclays

HCLTech

Tech Mahindra

Hexaware

Coforge

Birlasoft

Publicis Sapient

GlobalLogic

NTT DATA

Genpact

EXL

Samsung

Cyient

Happiest Minds Technologies

Infogain

Pine Labs

Ericsson

Nokia

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