Genpact Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Genpact hiring for Data Science roles
Genpact is a professional services company that began as a unit of General Electric in India. It provides business process management, finance and accounting operations, analytics, data and AI services. In India it hires for process associates, business analysts, data analysts, data engineers and data scientists.
For Data Science roles, this Genpact practice set focuses most on Statistics and Probability (9), Python for Data Science (7), Machine Learning (6), Feature Engineering (5). It has 23 easy, 17 medium questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
New York, USA
Main India offices
Gurugram, Hyderabad, Bengaluru, Noida, Kolkata
Genpact Data Science interview process
Online assessment (aptitude + English)
Technical or domain interview
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
What is the difference between image classification, object detection and image segmentation?
Classification assigns one label to the whole image, such as cat. Object detection finds each object and returns a bounding box plus a class label and confidence for each one. Segmentation labels every pixel, giving exact object shapes. The tasks need increasingly detailed annotations and compute. Choose based on the business need: counting products on a shelf needs detection, while measuring a crack's area needs segmentation.
02
What is the difference between one-hot, label and ordinal encoding?
One-hot encoding creates a binary column per category and suits nominal data like city for linear models. Label encoding maps categories to integers arbitrarily; it is fine for tree models but implies a false order for linear models. Ordinal encoding also uses integers but in a meaningful order, like low, medium, high mapped to 0, 1, 2. Choose based on whether categories have a natural order and which model you use.
Early stopping monitors a validation metric during training and stops when it has not improved for a set number of rounds, called patience. It is used in gradient boosting and neural networks to prevent overfitting and save time. The model from the best round is kept. The validation set must not be the final test set, otherwise the reported test score becomes optimistic.
04
What is a model registry and why do teams use one?
A model registry is a central store for trained model versions along with their metadata: training data version, code commit, parameters, metrics and owner. Models move through stages such as staging, production and archived, with approvals. It gives one source of truth for which model is live, makes rollback to a previous version easy and supports audits. Tools like MLflow Model Registry are commonly used.
05
Write SQL to show each customer's first order date, last order date and the gap between them in days.
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
It checks whether observed category counts differ from expected counts, used for independence between two categorical variables and for goodness of fit.
07
How do you handle rare categories in a categorical feature?
Categories that appear only a few times give unreliable signals and create many sparse columns. A common fix is to group all categories below a frequency threshold, like 1 percent, into an Other bucket. You can also use frequency encoding or smoothed target encoding. Make sure the encoder handles unseen categories at prediction time, for example with handle_unknown set to ignore.
*args collects extra positional arguments into a tuple; **kwargs collects extra keyword arguments into a dict. They make function signatures flexible and are common in wrappers and decorators.
10
How do you use groupby with multiple aggregations?
List: ordered and mutable. Tuple: ordered and immutable (hashable, slightly faster). Set: unordered, unique items, fast membership checks. Dict: key–value pairs with fast lookup by key.
12
What is the F1 score and when can it be misleading?
F1 is the harmonic mean of precision and recall, so it is high only when both are high. It is useful for imbalanced classes where accuracy hides poor performance. It can mislead because it ignores true negatives and treats precision and recall as equally important. If missing a fraud case costs far more than a false alarm, use an F-beta score with beta above 1, or choose metrics based on real costs.
A measure of asymmetry. Right-skewed data has a long tail on the right (mean greater than median); left-skewed is the opposite. Log transforms often reduce right skew.
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.
Named entity recognition finds and labels spans of text such as person names, organisations, locations, dates and amounts. For example, in an invoice email it can pull out the vendor, invoice number and total. It is usually framed as token classification with BIO tags. spaCy and fine-tuned transformer models are common tools. Domain-specific entities, like medicine names, usually need custom labelled training data.
16
A Pandas merge suddenly returns many more rows than the left table had. What happened?
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.
17
What are polynomial features and what is the risk of using them?
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.
18
What do kernel size, stride and padding mean in a convolution layer?
The kernel is a small learnable filter, such as 3x3, that slides over the image and computes a weighted sum at each position. Stride is how many pixels it moves each step; stride 2 halves the output size. Padding adds border pixels so the output can keep the same size and edge pixels get processed. Output size equals (input minus kernel plus 2 times padding) divided by stride, plus one.
Split the data into k folds, train on k−1 folds and validate on the remaining one, rotate and average the scores. It gives a more reliable estimate than one split and helps tune hyperparameters without touching the test set.
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?
22
What is the difference between saving a model with pickle, joblib and ONNX?
pickle serialises Python objects; it is simple but tied to Python and library versions, and loading untrusted pickle files is a security risk. joblib is similar but more efficient for objects with large NumPy arrays, like scikit-learn models. ONNX is an open, language-neutral format that runs in optimised runtimes across languages and hardware, useful for fast serving. Always store library versions alongside pickle or joblib files.
23
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.
Binning groups a continuous variable into ranges, like age into 18-25, 26-35 and so on. It can capture non-linear effects in linear models, reduce the impact of outliers and make results easier to explain to business teams. Bins can be equal width, equal frequency using quantiles, or chosen by domain knowledge. The downside is losing information inside each bin, and tree models usually do not need it.
25
What are interaction features and when do they help?
Interaction features combine two or more variables, such as price times quantity, or income divided by number of dependents. They help linear models capture effects where one variable changes the impact of another. Tree models can learn some interactions themselves, but explicit ratios often still help. Too many automatic interactions explode the feature count and overfit, so build them from domain logic and validate each one.
A forest averages many trees trained on bootstrapped rows and random feature subsets, which cuts variance and overfitting at the cost of interpretability.
27
What is chunking in RAG and how do you choose a chunk size?
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.
28
Tell me about a time your analysis contradicted what leadership expected.
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.
29
What is the binomial distribution and when do you use it?
The binomial distribution gives the probability of getting k successes in n independent trials, each with the same success probability p. Its mean is n times p and its variance is n times p times (1 minus p). You use it for counts of yes-or-no outcomes, such as how many of 1,000 visitors convert or how many of 50 parts are defective. For large n it is well approximated by a normal distribution.
Pooling downsamples a feature map by summarising small regions. Max pooling keeps the largest value in each window, such as 2x2, and average pooling keeps the mean. It reduces computation, enlarges the receptive field of later layers and gives a little tolerance to small shifts. Many modern networks replace pooling with strided convolutions, but global average pooling before the classifier head is still very common.
31
Tell me about a time you improved messy code written by someone else.
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.
32
What do temperature and top-p control in LLM generation?
Both control randomness when the model picks the next token. Temperature scales the probabilities: low values like 0 to 0.3 make output focused and repeatable, high values make it more varied and creative. Top-p, nucleus sampling, keeps only the smallest set of tokens whose probabilities add up to p, such as 0.9, and samples from them. For extraction and classification use low temperature; for brainstorming use higher values.
33
What is standard error and how is it different from standard deviation?
Standard deviation measures how spread out individual data points are. Standard error measures how much a sample statistic, usually the mean, would vary if you repeated the sampling. For the mean, SE = SD divided by the square root of n. So with more data the standard deviation stays roughly the same, but the standard error shrinks. Confidence intervals and t-tests are built on the standard error.
MLOps is the set of practices that take machine learning models from experiments to reliable production and keep them working. It covers versioning data and code, tracking experiments, automated training pipelines, testing, deployment, monitoring and retraining. It borrows ideas from DevOps but adds data and model concerns, because a model can fail even when the code has not changed, simply because the data has.
36
Why is cross-entropy preferred over MSE for classification in neural networks?
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.
37
How do you check whether a variable is normally distributed?
Start visually with a histogram and a Q-Q plot; points close to the straight line suggest normality. Check skewness and kurtosis values. Then use a formal test such as Shapiro-Wilk for small samples or Anderson-Darling. Remember that with very large samples, tests flag tiny harmless deviations, so judge by the plots too. If data is skewed, consider a log transform or a non-parametric method.
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.
Pearson measures linear relationships on continuous data; Spearman uses ranks, so it captures monotonic relationships and is less sensitive to outliers.
40
What is the difference between apply, map and applymap in Pandas?
map works element-wise on a Series, apply works on a Series or along a DataFrame axis, and applymap (now DataFrame.map) applies a function to every cell.
Tips to crack the Genpact Data Science interview
Learn process improvement basics like Lean and Six Sigma, which have long been part of Genpact's operating style.
Revise Excel, SQL and basic statistics for analytics roles, with examples of reporting or automation you built.
Read about Genpact's origins at GE and its finance and accounting outsourcing business for company questions.
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; Genpact interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Genpact Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + English); Technical or domain 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 Genpact Data Science interview?
This practice set leans on Statistics and Probability (9), Python for Data Science (7), Machine Learning (6), Feature Engineering (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Genpact interviewers ask you to walk through real work.
Are these the exact questions asked at Genpact?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Genpact's. They are not a leaked or verified list of past questions.
Where does Genpact hire Data Science professionals in India?
Genpact has large teams in Gurugram, Hyderabad, Bengaluru, Noida, Kolkata. Openings and locations change often, so check the Genpact careers page and filter by city and experience.