ICICI Bank Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About ICICI Bank hiring for Data Science roles
ICICI Bank is a large Indian private sector bank offering retail, corporate, digital and international banking. Its technology, analytics and operations teams work on products like iMobile Pay, credit decisioning, fraud detection and customer analytics. It hires for data analysts, risk analysts, testers, software engineers and management trainees.
For Data Science roles, this ICICI Bank practice set focuses most on Feature Engineering (6), Machine Learning (5), Time Series & Forecasting (5), Statistics and Probability (5). It has 7 easy, 28 medium, 5 hard questions, so you can start easy and work up to the harder ones.
Company type
Banking & finance
Headquarters
Mumbai, India
Main India offices
Mumbai, Hyderabad, Chennai, Bengaluru
ICICI Bank Data Science interview process
Online assessment (aptitude + domain)
Technical or functional interview
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for ICICI BankStart practicing
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Function calling lets an LLM respond with a structured request to call a tool you defined, such as get_order_status with an order_id, instead of plain text. Your code runs the function, returns the result to the model, and the model uses it in its final answer. This connects LLMs to live data, calculators and APIs. You must validate arguments, limit which tools exist and handle errors, because the model can choose wrong inputs.
Use grid search, random search or Bayesian optimisation with cross-validation, starting with a coarse random search and narrowing the promising ranges.
Convert strings with pd.to_datetime, giving a format argument for speed and safety. Use the .dt accessor to pull year, month, weekday or hour. For time zones, first tz_localize naive timestamps to their real source zone, then tz_convert to a common zone such as UTC or Asia/Kolkata. Store data in UTC and convert only for reporting. Mixing naive and aware timestamps causes errors, so keep one convention across the pipeline.
04
You must classify support messages into 40 categories, but some categories have only 20 examples. What do you do?
Start with a pretrained sentence embedding model plus a simple classifier, or fine-tune a small transformer with class weights. For the rare categories, try few-shot approaches such as SetFit or an LLM with examples in the prompt, and generate or collect more examples. Merge categories that are rarely distinguishable. Report per-class F1 rather than overall accuracy, and route low-confidence predictions to a human queue.
A function using yield that produces values lazily, one at a time. It saves memory when processing large files or streams because the full sequence is never held in RAM.
The rule that lets arrays of different shapes operate together by virtually stretching the smaller one, for example adding a scalar or a (3,) vector to a (4,3) matrix.
07
Which activation functions do you know and when to use them?
ReLU for hidden layers, sigmoid for binary outputs, softmax for multi-class outputs and tanh when zero-centred outputs help; Leaky ReLU avoids dead neurons.
08
You must forecast demand for a new product that has only three weeks of history. What do you do?
Three weeks is too little to learn seasonality, so borrow information. Find similar past launches by category, price band and channel, and use their early sales curves as analogues. A global model trained across many products, with product attributes as features, can also forecast a cold-start item. Combine this with planned marketing spend and business judgement, show a wide interval, and update the forecast weekly as real data arrives.
09
What is the difference between a population and a sample?
A population is the full group you want to learn about, such as all customers of an app. A sample is the subset you actually observe, such as 5,000 surveyed customers. Numbers from the population are parameters, and numbers from a sample are statistics that estimate them. Good sampling, ideally random, lets you generalise from the sample to the population and measure the uncertainty using standard errors and confidence intervals.
10
Tell me about a time you had to deliver data under an urgent deadline.
Situation: the leadership team needed festive-sale numbers by city for a 9 am review, and the dashboard pipeline had failed overnight. Task: deliver accurate numbers in three hours. Action: I queried the raw orders tables directly, reused validated logic from the dashboard SQL, cross-checked totals with finance's daily figure and flagged two cities with partial data. Result: the review went ahead on time, and I later added pipeline failure alerts.
11
After one-hot encoding you have 2,000 features and training is slow. What do you do?
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.
12
Write Pandas code to resample daily sales into weekly totals and monthly averages.
Situation: during my internship I was building a monthly sales report. Task: make sure numbers were correct before sharing. Action: I compared order counts per day and noticed one day had double the usual orders. A GROUP BY on order_id showed every order that day appeared twice because a load job had run twice. Result: I reported it, the data team removed duplicates and added a uniqueness check. Now I always validate row counts.
14
What do ACF and PACF plots tell you about a time series?
The ACF plot shows correlation between the series and its own lags, including indirect effects through intermediate lags. The PACF shows the correlation at each lag after removing the effect of shorter lags. For a stationary series, a PACF that cuts off after lag p suggests an AR(p) term, and an ACF that cuts off after lag q suggests an MA(q) term. Spikes at lag 7 or 12 reveal weekly or yearly seasonality.
15
What is the difference between a star schema and a snowflake schema?
A star schema has a central fact table, like sales with amounts and foreign keys, surrounded by denormalised dimension tables such as product, customer and date. A snowflake schema normalises dimensions further, for example product links to a separate category table. Star schemas need fewer joins and are faster and simpler for analysts. Snowflake schemas save storage and reduce duplication but make queries more complex.
The ADF test checks whether a series has a unit root, which means it is non-stationary. The null hypothesis is that a unit root exists. A small p-value, say below 0.05, lets you reject the null and treat the series as stationary. It is often paired with the KPSS test, whose null is the opposite, stationarity. Using both gives a more reliable conclusion before choosing how much to difference.
17
How do you evaluate clustering when you have no labels?
Use internal metrics: the silhouette score compares how close a point is to its own cluster versus the nearest other cluster, and the Davies-Bouldin index measures cluster overlap. The elbow method looks at within-cluster variance. But the real test is usefulness: are clusters stable across random seeds and samples, can the business describe them, and do they behave differently on outcomes like spend or churn?
18
You target-encoded a city column and validation AUC jumped from 0.72 to 0.95. What do you suspect?
Target encoding replaces each category with the average target value for that category, for example the churn rate per city. It handles high-cardinality columns compactly. The main risk is leakage: if a row's own target is included in its encoding, the model sees the answer and overfits. Prevent it with out-of-fold encoding, smoothing toward the global mean for rare categories, and fitting only on training data.
19
Write torchvision transforms for training and validation of an image classifier using ImageNet normalisation.
An OEC is a single agreed metric, or a weighted combination of metrics, that represents long-term business value and is used to judge experiments. Good OECs are sensitive enough to move within a test's duration yet aligned with long-term goals; for example, sessions per user with penalties for churn rather than raw clicks. Having an OEC prevents teams from each optimising their own metric and makes ship decisions consistent across the organisation.
21
What is multicollinearity and how do you handle it?
Predictors that are highly correlated make coefficients unstable. Detect with VIF above about 5-10, then drop or combine features, use PCA or apply regularisation.
22
What is SARIMA and how does it extend a plain ARIMA model?
SARIMA adds seasonal terms to ARIMA. It is written ARIMA(p,d,q)(P,D,Q)m, where the second set are seasonal AR, differencing and MA orders, and m is the season length, such as 7 for daily data with a weekly cycle or 12 for monthly data. SARIMAX further allows external regressors like price or holidays. It handles one seasonal period well but struggles with multiple seasonalities, such as daily plus yearly.
23
What are quantile and power transforms such as Box-Cox and Yeo-Johnson, and when do you use them?
Power transforms reshape a skewed feature to look more normal. Box-Cox needs strictly positive values, while Yeo-Johnson also handles zero and negative values; both learn a lambda parameter from training data. A quantile transform maps values to a uniform or normal distribution by rank, which is robust to outliers. They help linear models, distance-based methods and neural networks, but tree models rarely need them.
Retrieval-Augmented Generation fetches relevant documents and gives them to an LLM as context, reducing hallucination and keeping answers grounded in current private data.
25
Write SQL to compute a 7-day rolling average of daily active users.
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;
26
Write NumPy code for non-maximum suppression on a set of boxes and scores.
Situation: our delivery time model kept underestimating late-night orders. Task: reduce large errors. Action: I noticed restaurant prep time depended on how busy the kitchen was, so I built a feature for active orders at that restaurant in the last 30 minutes, computed point in time. Result: MAE dropped by 14 percent and late-night errors fell the most. It taught me to study the worst errors before adding generic features.
29
What is DBSCAN and when is it better than K-means?
DBSCAN is a density-based clustering algorithm. It groups points that have at least min_samples neighbours within distance eps, and marks isolated points as noise. Unlike K-means, it does not need the number of clusters, finds clusters of any shape, and handles outliers. It struggles when clusters have very different densities and in high dimensions. It suits location data, like finding delivery hotspots from GPS points.
30
What is the difference between parametric and non-parametric tests?
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.
32
What is the randomisation unit in an experiment, and why does it matter?
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.
33
Your product image classifier works on studio catalogue photos but fails on photos users take with their phones. Why, and what do you do?
This is domain shift: user photos have clutter, blur, poor lighting and odd angles that the model never saw. Collect and label a sample of real user photos and evaluate on them separately. Add augmentation for blur, noise, colour jitter, rotation and random crops, and fine-tune on a mix of studio and real images. Monitor accuracy on user photos in production, since that is the distribution that matters.
34
Your A/B test shows a significant lift after 2 days. The product team wants to ship now. What do you say?
I would ask them to wait for the planned duration. Checking early and stopping on the first significant result, called peeking, inflates false positives. Two days also misses weekly patterns and novelty effects, where users click a new feature just because it is new. If speed matters, we can use a sequential testing method designed for early stopping. Otherwise we run the full sample size calculated before launch.
35
How do you mentor junior data scientists on your team?
Situation: two freshers joined my team and struggled with messy real data. Task: help them deliver independently. Action: I paired with them on the first project, set up weekly code reviews, shared a checklist for validation and leakage, and gave them small end-to-end tasks with clear success metrics. Result: within three months both owned models in production. I focus on teaching how to debug and ask questions, not just giving answers.
36
What are precision and recall trade-offs in practice?
Raising the threshold improves precision but lowers recall. Fraud detection leans to recall, while spam filters often prefer precision; choose by the cost of each error.
37
A feature was launched to all users with no control group. How would you estimate its causal impact?
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.
38
Write Keras code for a small binary classifier on tabular data.
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.
Tips to crack the ICICI Bank Data Science interview
Learn Indian banking basics such as loan types, NPAs, CASA ratio and RBI regulations for interviews.
Use iMobile Pay and think about features you would test or improve as a QA or analyst candidate.
Revise SQL and Excel with examples on customer segmentation, credit risk or campaign analysis.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; ICICI Bank interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the ICICI Bank Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + domain); Technical or functional interview; Managerial round; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the ICICI Bank Data Science interview?
This practice set leans on Feature Engineering (6), Machine Learning (5), Time Series & Forecasting (5), Statistics and Probability (5). Revise those first, then practise explaining one or two of your own projects end to end, because most ICICI Bank interviewers ask you to walk through real work.
Are these the exact questions asked at ICICI Bank?
No. These are curated practice questions that match the skills and difficulty usually tested in banking & finance interviews like ICICI Bank's. They are not a leaked or verified list of past questions.
Where does ICICI Bank hire Data Science professionals in India?
ICICI Bank has large teams in Mumbai, Hyderabad, Chennai, Bengaluru. Openings and locations change often, so check the ICICI Bank careers page and filter by city and experience.