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Data Science Interview Questions in Hyderabad

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

Data Science jobs in Hyderabad

Hyderabad has large engineering and capability centres of global technology, banking and pharma companies around HITEC City and the Financial District. Many roles are with global capability centres that work with teams in the US and Europe.

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

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Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.

Machine LearningMedium

Show answer

from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.calibration import CalibratedClassifierCV from sklearn.metrics import brier_score_loss base = HistGradientBoostingClassifier() cal = CalibratedClassifierCV(base, method='isotonic', cv=5) cal.fit(X_train, y_train) prob = cal.predict_proba(X_test)[:, 1] print('Brier:', brier_score_loss(y_test, prob))

Write a FastAPI endpoint that serves predictions from a saved scikit-learn model.

MLOps and Model DeploymentMedium

Show answer

from fastapi import FastAPI from pydantic import BaseModel import joblib import pandas as pd app = FastAPI() model = joblib.load('model.joblib') class Customer(BaseModel): tenure: int monthly_spend: float plan: str @app.post('/predict') def predict(c: Customer): X = pd.DataFrame([c.model_dump()]) return {'churn_probability': float(model.predict_proba(X)[0, 1])}

What is the Global Interpreter Lock (GIL)?

Python for Data ScienceMedium

Show answer

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 gradient descent and what does the learning rate do?

Machine LearningMedium

Show answer

An optimisation method that updates weights in the opposite direction of the loss gradient. The learning rate sets the step size: too high and it diverges, too low and it is slow. Variants include SGD, mini-batch and Adam.

What is statistical power and how can you increase it?

Statistics and ProbabilityMedium

Show answer

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.

A Pandas merge suddenly returns many more rows than the left table had. What happened?

Python for Data ScienceMedium

Show answer

The join key is probably not unique on the right side, so each left row matches several right rows and the result multiplies. Check with right['key'].duplicated().sum(). Other causes are null or blank keys matching each other after cleaning, or joining on the wrong column. Fix it by deduplicating or aggregating the right table first, and use merge(..., validate='many_to_one') so Pandas raises an error when the assumption breaks.

What is subword tokenisation like BPE or WordPiece?

Deep Learning and NLPMedium

Show answer

Subword tokenisers split text into frequent pieces between characters and full words. Byte Pair Encoding starts from characters and repeatedly merges the most frequent pairs. WordPiece, used by BERT, is similar but chooses merges by likelihood. Rare words like unhappiness become un, happi, ness, so there are no unknown words and the vocabulary stays around 30,000 to 100,000. Token counts also decide LLM cost and context use.

A key metric dropped 8 percent week over week. How do you check whether it is real?

Statistics and ProbabilityMedium

Show answer

First rule out data issues: tracking changes, pipeline delays, missing partitions or a changed metric definition. Then compare with the same week last year and look for holidays or seasonality. Check normal weekly variation to see if 8 percent is unusual. Break the drop down by platform, region, app version and user type to find where it is concentrated. Finally, check releases, campaigns or outages that happened that week.

Write a prompt template for classifying customer support tickets.

Generative AI and LLMsMedium

Show answer

System: You classify support tickets for an e-commerce company. Categories: payment, delivery, refund, account, other. Rules: choose exactly one category; if unsure, choose other; do not invent details. Return JSON with fields category and reason, reason under 20 words. Then two short examples showing a ticket and the expected JSON. User: Ticket: {ticket_text}. Keep the template fixed and test it on 100 labelled tickets before rollout.

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.

What is recursive feature elimination?

Feature EngineeringMedium

Show answer

Recursive feature elimination trains a model, ranks features by importance or coefficient size, removes the weakest ones, and repeats until the desired number remains. RFECV picks the number of features using cross-validation. It considers features together rather than one at a time. The downsides are high compute cost on large data and unstable results when features are correlated, so check stability across folds.

Pearson vs Spearman correlation?

Statistics and ProbabilityEasy

Show answer

Pearson measures linear relationships on continuous data; Spearman uses ranks, so it captures monotonic relationships and is less sensitive to outliers.

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

Time Series & ForecastingMedium

Show answer

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.

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;

Write SQL to find duplicate emails in a users table.

SQL for Data ScienceEasy

Show answer

SELECT email, COUNT(*) AS cnt FROM users GROUP BY email HAVING COUNT(*) > 1 ORDER BY cnt DESC;

Write SQL to pivot order counts by status into columns using CASE WHEN.

SQL for Data ScienceEasy

Show answer

SELECT DATE_TRUNC('month', order_date) AS month, SUM(CASE WHEN status = 'delivered' THEN 1 ELSE 0 END) AS delivered, SUM(CASE WHEN status = 'cancelled' THEN 1 ELSE 0 END) AS cancelled, SUM(CASE WHEN status = 'returned' THEN 1 ELSE 0 END) AS returned FROM orders GROUP BY 1 ORDER BY 1;

Write Python code to decompose a monthly series into trend, seasonal and residual parts with STL.

Time Series & ForecastingEasy

Show answer

import pandas as pd from statsmodels.tsa.seasonal import STL s = df.set_index('month')['sales'].asfreq('MS') res = STL(s, period=12, robust=True).fit() parts = pd.DataFrame({ 'trend': res.trend, 'seasonal': res.seasonal, 'resid': res.resid }) print(parts.tail()) res.plot()

What is the difference between mean, median and mode?

Statistics and ProbabilityEasy

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Mean is the average, median is the middle value after sorting and mode is the most frequent value. Median is more robust to outliers.

After one-hot encoding you have 2,000 features and training is slow. What do you do?

Feature EngineeringMedium

Show answer

Find which columns caused the explosion, usually a few high-cardinality categoricals. Group rare categories into Other, or switch those columns to frequency, target or hashing encoding. Use sparse matrices so zeros take no memory. Tree libraries like LightGBM and CatBoost can handle categoricals natively without one-hot. Then remove near-constant columns and check that validation scores hold up with the smaller feature set.

What are polynomial features and what is the risk of using them?

Feature EngineeringMedium

Show answer

Polynomial features add powers and products of existing features, like x squared and x times y, so a linear model can fit curves and interactions. The risk is that the number of features grows very fast with degree and input count, causing overfitting, slow training and multicollinearity. Use a low degree, scale features first, and add regularisation such as Ridge or Lasso to keep coefficients under control.

Random forest vs a single decision tree?

Machine LearningMedium

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A forest averages many trees trained on bootstrapped rows and random feature subsets, which cuts variance and overfitting at the cost of interpretability.

What are lambda functions and where are they useful?

Python for Data ScienceMedium

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Small anonymous one-line functions, handy as arguments to sort, map, filter or Pandas apply.

What is the difference between Bayesian and frequentist approaches?

Statistics and ProbabilityHard

Show answer

Frequentists treat parameters as fixed and use long-run frequencies; Bayesians treat parameters as distributions updated with data via priors and posteriors, giving direct probability statements.

A 10 million row DataFrame with a city column is using too much memory. How can the category dtype help?

Python for Data ScienceMedium

Show answer

The category dtype stores each unique value once and keeps small integer codes for every row. For a column like city with 50 unique values in 10 million rows, this can cut memory by more than 90 percent and speed up groupby and sorting. It helps when a column has low cardinality. It does not help for unique IDs or free text, and adding new categories later needs extra handling.

How would you quickly build a sentiment analysis model for product reviews?

Deep Learning and NLPEasy

Show answer

Step 1: collect and label a sample of reviews, checking class balance. Step 2: build a baseline with TF-IDF and logistic regression to get a quick benchmark. Step 3: fine-tune a small pretrained transformer such as DistilBERT for better accuracy, or use a multilingual model if reviews mix languages. Step 4: evaluate with F1 per class and read wrong predictions. Step 5: handle sarcasm and neutral reviews explicitly.

Your test shows a 2 percent lift in conversion, but revenue per user went down. What do you do?

A/B Testing & ExperimentationMedium

Show answer

Do not ship on conversion alone. Check whether the change shifted buyers to cheaper items, increased discount usage or pulled in low-value orders. Break down revenue into conversion times average order value, look at item mix, and confirm the revenue drop is statistically meaningful rather than noise. Then go back to the agreed primary metric and guardrails; if revenue is the business goal, it should guide the decision.

Tell me about a time you improved messy code written by someone else.

Python for Data ScienceEasy

Show answer

Situation: in my final-year project, a teammate's cleaning script was one 600-line notebook that took 20 minutes to run. Task: I needed to reuse it for new data. Action: I split it into small functions, added docstrings, replaced row loops with vectorised Pandas code and wrote a few checks for row counts. Result: run time fell to 2 minutes and the team could reuse the functions. I learned to refactor in small, tested steps.

What is the difference between hard voting and soft voting in ensembles?

Machine LearningMedium

Show answer

In hard voting each model predicts a class and the majority wins. In soft voting each model gives class probabilities, they are averaged, and the class with the highest average wins. Soft voting usually works better because it uses each model's confidence, but it needs calibrated probabilities from all models. Hard voting is simpler and works even with models that only output labels.

Tell me about a time you deployed a model under a tight deadline.

MLOps and Model DeploymentMedium

Show answer

Situation: a pricing model had to go live before a sale in ten days. Task: deploy safely in that window. Action: I cut scope to batch scoring instead of real-time, reused our standard Docker and Airflow template, wrote tests for the key features and set up a dashboard for score distribution. Result: it went live two days early and ran without incidents during the sale. Real-time scoring followed a month later.

Write SQL to find customers who ordered in January 2026 but not in February 2026.

SQL for Data ScienceMedium

Show answer

SELECT DISTINCT o.customer_id FROM orders o WHERE o.order_date >= '2026-01-01' AND o.order_date < '2026-02-01' AND NOT EXISTS ( SELECT 1 FROM orders f WHERE f.customer_id = o.customer_id AND f.order_date >= '2026-02-01' AND f.order_date < '2026-03-01' );

Write a Dockerfile for a Python model API.

MLOps and Model DeploymentMedium

Show answer

FROM python:3.11-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY app/ ./app COPY model.joblib . EXPOSE 8000 CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

What is the difference between zero-shot and few-shot prompting?

Generative AI and LLMsEasy

Show answer

In zero-shot prompting you give only instructions, like classify this review as positive or negative. In few-shot prompting you also include a few worked examples of input and correct output, which shows the model the format and decision boundary. Few-shot usually improves consistency on custom labels, at the cost of more tokens. Pick examples that cover tricky cases and keep their format identical to what you want back.

How do you get reliable JSON output from an LLM?

Generative AI and LLMsMedium

Show answer

Use the provider's structured output or JSON schema mode when available, which constrains the output to your schema. Otherwise, describe the exact schema in the prompt with an example, and use low temperature. Always parse and validate the response in code, for example with Pydantic, and on failure retry with the error message included. Keep schemas small and flat, and use enums for fields with fixed values.

What is correlation and why does it not imply causation?

Statistics and ProbabilityMedium

Show answer

Correlation measures linear association between variables (-1 to 1). A third factor, reverse causality or chance can create it, so causation needs controlled experiments.

Write Python code to compute the Population Stability Index for a feature to detect drift.

MLOps and Model DeploymentHard

Show answer

import numpy as np def psi(expected, actual, bins=10): edges = np.quantile(expected, np.linspace(0, 1, bins + 1)) edges[0], edges[-1] = -np.inf, np.inf e = np.histogram(expected, edges)[0] / len(expected) a = np.histogram(actual, edges)[0] / len(actual) e, a = np.clip(e, 1e-6, None), np.clip(a, 1e-6, None) return float(np.sum((a - e) * np.log(a / e))) # rule of thumb: <0.1 stable, 0.1-0.25 watch, >0.25 investigate

How do you pivot data in Pandas?

Python for Data ScienceMedium

Show answer

pd.pivot_table(df, index='region', columns='month', values='sales', aggfunc='sum') reshapes long data into a summary matrix; melt() does the reverse.

Companies with offices in Hyderabad (56)

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Data Science interview questions

40 questions
4 rounds

Mumbai, Chennai, Pune +2

Data Science Interview Questions in Hyderabad

Data Science jobs in Hyderabad

Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.

Write a FastAPI endpoint that serves predictions from a saved scikit-learn model.

What is the Global Interpreter Lock (GIL)?

What is gradient descent and what does the learning rate do?

What is statistical power and how can you increase it?

A Pandas merge suddenly returns many more rows than the left table had. What happened?

What is subword tokenisation like BPE or WordPiece?

A key metric dropped 8 percent week over week. How do you check whether it is real?

Write a prompt template for classifying customer support tickets.

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

What is recursive feature elimination?

Pearson vs Spearman correlation?

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

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

Write SQL to find duplicate emails in a users table.

Write SQL to pivot order counts by status into columns using CASE WHEN.

Write Python code to decompose a monthly series into trend, seasonal and residual parts with STL.

What is the difference between mean, median and mode?

After one-hot encoding you have 2,000 features and training is slow. What do you do?

What are polynomial features and what is the risk of using them?

Random forest vs a single decision tree?

What are lambda functions and where are they useful?

What is the difference between Bayesian and frequentist approaches?

A 10 million row DataFrame with a city column is using too much memory. How can the category dtype help?

How would you quickly build a sentiment analysis model for product reviews?

Your test shows a 2 percent lift in conversion, but revenue per user went down. What do you do?

Tell me about a time you improved messy code written by someone else.

What is the difference between hard voting and soft voting in ensembles?

Tell me about a time you deployed a model under a tight deadline.

Write SQL to find customers who ordered in January 2026 but not in February 2026.

Write a Dockerfile for a Python model API.

What is the difference between zero-shot and few-shot prompting?

How do you get reliable JSON output from an LLM?

What is correlation and why does it not imply causation?

Write Python code to compute the Population Stability Index for a feature to detect drift.

How do you pivot data in Pandas?

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