Infogain Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Infogain hiring for Data Science roles
Infogain is a digital engineering and IT services company serving clients in technology, travel, retail, insurance and healthcare. It offers software engineering, cloud, data and analytics, experience design and customer engagement solutions. It is part of the Apax Funds portfolio and has large delivery centres in India, where it hires developers, testers, data engineers and cloud specialists for global client projects.
For Data Science roles, this Infogain practice set focuses most on Deep Learning and NLP (6), Python for Data Science (5), Generative AI and LLMs (4), MLOps and Model Deployment (4). It has 15 easy, 23 medium, 2 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
California, USA
Main India offices
Noida, Gurugram, Pune, Bengaluru
Infogain Data Science interview process
Online assessment (aptitude + technical MCQs)
Technical interview
Client or 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 a Pandas Series and a DataFrame?
A Series is a one-dimensional labelled array: one column of values with an index. A DataFrame is a two-dimensional table made of several Series that share the same index, and each column can have its own dtype. Selecting one column, like df['price'], gives you a Series. Selecting with a list, like df[['price']], gives you a DataFrame. Many methods work on both, but the output shape is different.
02
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.
03
What does CI/CD mean for machine learning projects?
Continuous integration runs automatic checks on every code change: unit tests for feature code, data validation, linting and a quick training run on sample data. Continuous delivery packages the model and service, for example as a Docker image, and deploys it through staging to production after evaluation gates, such as the new model beating the current one on a holdout set. Some teams add continuous training, which retrains on new data automatically.
04
How do you turn text columns into features for a tabular model?
Simple options are length, word count and keyword flags. TF-IDF vectors capture important words and work well with linear models. For meaning, use sentence embeddings from a pretrained model and optionally reduce them with PCA. You can also train a separate text classifier and feed its predicted score as one feature. Fit any vocabulary or encoder on training data only to avoid leakage.
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.
06
What is the difference between list, tuple, set and dict in Python?
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.
07
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.
08
What are embeddings and vector databases, and why do LLM apps use them?
An embedding model turns text into a vector so that similar meanings are close in space. A vector database stores these vectors with metadata and finds the nearest ones to a query vector quickly using approximate nearest neighbour indexes like HNSW. LLM apps use them for semantic search and retrieval-augmented generation, finding the most relevant documents to put into the prompt. Examples include pgvector, FAISS and dedicated vector stores.
09
Write Pandas code to create useful features from a datetime column.
Situation: each monthly retrain needed a day of manual data pulls, notebook runs and copying files to servers. Task: make it reliable and hands-off. Action: I wrapped the steps into a scheduled pipeline with data validation checks, logged runs in MLflow and added an approval step before promotion. Result: retraining time dropped from a day to under an hour of review, and there were no more failed deployments from missed steps.
11
What is the difference between probability and likelihood?
Probability fixes the parameters and asks how likely different data are, for example the chance of 7 heads in 10 tosses if the coin is fair. Likelihood fixes the observed data and asks how plausible different parameter values are, for example which coin bias best explains 7 heads. The likelihood function is not a probability distribution over parameters. Maximum likelihood estimation picks the parameter value that maximises it.
12
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.
A naive forecast predicts that the next value equals the last observed value. A seasonal naive forecast predicts the value from the same period last season, such as last Monday for next Monday. They take seconds to build and are surprisingly hard to beat on many business series. Always report your model's error relative to these baselines; if a complex model cannot beat seasonal naive, it is not adding value.
Putting features on comparable ranges using standardisation or min-max scaling. Distance-based and gradient-based models (KNN, SVM, regression, neural nets) need it; tree models do not.
16
Your image classifier trained on 2,000 images memorises the training set within 5 epochs. What do you try?
With so little data, I would use transfer learning from a pretrained model like ResNet or EfficientNet and fine-tune only the top layers first. I would add data augmentation such as flips, crops and colour changes, use dropout and weight decay, and apply early stopping on validation loss. Collecting more labelled images or using a smaller model also helps. I would check for duplicate images across train and validation sets too.
Tokens are the pieces of text an LLM reads and writes, often word parts; in English, one token is roughly three quarters of a word. The context window is the maximum number of tokens the model can consider at once, including your prompt, any documents and its answer. Costs and speed depend on token counts. Long documents beyond the window must be chunked, summarised or retrieved selectively.
18
Soon after a new model version went live, customer complaints spiked. How do you roll back safely and learn from it?
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.
19
Write Python code to compute the Population Stability Index for a feature to detect drift.
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.
In an A/A test both groups get the identical experience. Since there is no real difference, results should be significant only about 5 percent of the time at alpha 0.05. Running A/A tests validates your experimentation platform: it catches broken randomisation, logging bugs, sample ratio problems and metrics whose variance is wrongly estimated. Teams often run them when launching a new platform or a new metric.
Accuracy, F1 and confusion matrices for classification, BLEU or ROUGE for generation and summarisation, perplexity for language models, plus human review.
24
Write scikit-learn code to get precision, recall and a confusion matrix at a custom threshold of 0.3.
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.
27
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.
28
Write Python code to compare revenue per user between two groups using Welch's t-test.
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.
32
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.
33
What is the difference between an epoch, a batch and an iteration?
Several reasons are common. Winner's curse: significant results from underpowered tests tend to overestimate the true effect. Novelty effect faded after launch. Seasonality or other launches changed the baseline, so a before-after comparison is misleading. The test population may differ from all users, for example only one platform. Use a holdout after launch to measure real impact, and run tests with adequate power and duration.
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.
IoU measures how well a predicted box or mask overlaps the ground truth. It is the area of their intersection divided by the area of their union, giving a value from 0 for no overlap to 1 for a perfect match. In detection, a prediction usually counts as correct if IoU is at least 0.5 with a ground truth box of the same class. IoU is also used inside non-maximum suppression.
39
What is the difference between ROW_NUMBER, RANK and DENSE_RANK?
All three number rows within a window ordered by some column. ROW_NUMBER gives unique numbers even for ties: 1, 2, 3. RANK gives tied rows the same rank and skips the next numbers: 1, 1, 3. DENSE_RANK gives ties the same rank without gaps: 1, 1, 2. Use ROW_NUMBER to pick exactly one row per group, and DENSE_RANK for questions like the second highest distinct salary.
40
Write PyTorch code to fine-tune a pretrained ResNet-18 for a 5-class problem by replacing its final layer.
import torch, torch.nn as nn
from torchvision import models
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
for p in model.parameters():
p.requires_grad = False # freeze backbone
model.fc = nn.Linear(model.fc.in_features, 5)
opt = torch.optim.AdamW(model.fc.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()
# train head first, then unfreeze later layers with a lower lr
Tips to crack the Infogain Data Science interview
Revise one programming language, SQL and REST API basics, and be ready to solve a simple coding problem live.
Prepare examples of working in a team, handling deadlines and communicating clearly, since many projects involve direct client interaction.
Read about Infogain's focus on digital engineering and the industries it serves to answer why you want to join.
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; Infogain interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Infogain Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical MCQs); Technical interview; Client or 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 Infogain Data Science interview?
This practice set leans on Deep Learning and NLP (6), Python for Data Science (5), Generative AI and LLMs (4), MLOps and Model Deployment (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Infogain interviewers ask you to walk through real work.
Are these the exact questions asked at Infogain?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Infogain's. They are not a leaked or verified list of past questions.
Where does Infogain hire Data Science professionals in India?
Infogain has large teams in Noida, Gurugram, Pune, Bengaluru. Openings and locations change often, so check the Infogain careers page and filter by city and experience.