MakeMyTrip Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About MakeMyTrip hiring for Data Science roles
MakeMyTrip is an Indian online travel company offering flights, hotels, holidays, buses and trains, and it also owns Goibibo and redBus. Its technology and analytics teams work on search, pricing, personalisation and customer service. It hires for software engineers, SDETs, business analysts and data scientists.
For Data Science roles, this MakeMyTrip practice set focuses most on A/B Testing & Experimentation (6), MLOps and Model Deployment (6), Machine Learning (5), Deep Learning and NLP (5). It has 2 easy, 28 medium, 10 hard questions, so you can start easy and work up to the harder ones.
Company type
E-commerce & consumer tech
Headquarters
Gurugram, India
Main India offices
Gurugram, Bengaluru
MakeMyTrip Data Science interview process
Online assessment (coding or SQL)
Technical round 1
Technical round 2 or case round
Hiring manager round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Write Python code to compute the haversine distance between customer and store locations.
Not significant does not mean no effect. Report the observed effect with its confidence interval, for example minus 0.3 to plus 1.2 percent, and compare it with the MDE the test was powered for. If the interval rules out effects that matter, conclude the change has little impact and decide based on cost or design preference. If the interval is wide, the test was underpowered, and a longer test or variance reduction may help.
03
You added an index but the query is still slow. How do indexes work, and when do they fail to help or even hurt?
An index is a separate sorted structure, usually a B-tree, that lets the database find rows without scanning the whole table, like a book index. It speeds up filters, joins and sorting on the indexed columns. Indexes cost storage and slow down inserts, updates and deletes because each index must be updated. They may be ignored for low-selectivity columns or when a function is applied to the column in WHERE.
04
Tell me about a time you had to build a model when the requirements were unclear.
Situation: a sales head asked for a model to find good leads but could not define good. Task: turn this into a clear target. Action: I met the team, looked at past deals, and proposed converted within 60 days as the label, with expected revenue as a secondary view. I shared a quick baseline in a week to get feedback. Result: the definition was agreed, and the final model raised conversion in the top decile.
05
The business asks you to pick a decision threshold for a churn model. How do you choose it?
The default 0.5 is rarely right. I would get the cost of each outcome: the value of saving a churner, the cost of a retention offer, and the team's capacity to contact customers. Then I compute expected profit or precision and recall at many thresholds on validation data and pick the one that maximises value within capacity. I would also check that the chosen threshold stays stable over recent months.
06
What is the difference between recursive and direct multi-step forecasting?
In recursive forecasting you train a one-step model and feed each prediction back as an input to predict the next step, so errors compound over the horizon. In direct forecasting you train a separate model for each horizon, such as one for t+1 and another for t+7, which avoids compounding but costs more and may give inconsistent forecasts. Hybrid and multi-output approaches sit in between and are common in production.
07
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.
08
What is correlation and why does it not imply causation?
Correlation measures linear association between variables (-1 to 1). A third factor, reverse causality or chance can create it, so causation needs controlled experiments.
09
You are building defect detection for a factory line where only 1 in 1,000 images shows a defect. How do you approach it?
With so few defects, a plain classifier will learn to say no defect. Collect every defect image possible and oversample them or use class-weighted or focal loss. Consider anomaly detection trained only on good parts, which flags anything unusual. Use augmentation that mimics real variation in lighting and position. Evaluate with recall at a fixed false alarm rate, since missing a defect usually costs far more than a manual recheck.
10
Write a basic PyTorch training loop for one epoch.
model.train()
for X, y in train_loader:
X, y = X.to(device), y.to(device)
optimizer.zero_grad()
logits = model(X)
loss = loss_fn(logits, y)
loss.backward()
optimizer.step()
# evaluation
model.eval()
with torch.no_grad():
val_loss = sum(loss_fn(model(Xv.to(device)), yv.to(device)).item() for Xv, yv in val_loader)
11
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.
12
Your LLM feature costs far more than planned. How do you reduce cost without hurting quality?
Measure cost per request and find the biggest token users. Shorten system prompts and retrieved context, and cap output length. Route simple requests to a smaller, cheaper model and keep the large model for hard ones. Cache repeated answers and use provider prompt caching for fixed prefixes. Batch offline jobs. Consider fine-tuning a small model for a high-volume narrow task. Check every change against the evaluation set.
13
What is Bayesian A/B testing and how are its results reported?
Bayesian A/B testing starts with a prior on each variant's metric, updates it with observed data and produces a posterior distribution. Instead of a p-value, you report things like the probability that treatment beats control, say 94 percent, and the expected loss if you choose the wrong variant. Business teams find this easier to interpret. Priors must be chosen sensibly, and continuous monitoring still needs discipline about decision rules.
Define the hypothesis and one primary metric, calculate sample size from the baseline, minimum detectable effect, α and power, randomise users, run for full business cycles, check for sample-ratio mismatch, then test both statistical and practical significance.
15
How do you choose a learning rate for a neural network?
Add a classification head, freeze or gradually unfreeze layers, use a small learning rate, train on labelled data with validation, and track F1 per class.
17
Write Python code that validates prediction requests with Pydantic before passing them to a model.
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.
I would first align definitions: date range, time zone, filters like test users or cancelled orders, and whether the metric counts users or events. Then compare at a smaller grain, by day or by city, to find where the gap starts. I would read the dashboard's underlying query and check data refresh times. Once found, I would document the agreed definition so the same mismatch does not repeat.
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.
22
What is CUPED and how does it reduce variance in experiments?
CUPED uses a pre-experiment covariate, usually the same metric for each user in the weeks before the test, to remove predictable variation. The adjusted metric is Y minus theta times (X minus the mean of X), where theta is cov(X, Y) divided by var(X). Because randomisation makes X balanced across groups, the estimate stays unbiased while variance can drop by 30 to 50 percent, letting tests reach significance faster with the same traffic.
23
A new checkout converts better on both mobile and desktop, but worse overall. How is that possible?
Simpson's paradox happens when a trend in several groups reverses when the groups are combined. Example: a new checkout converts better on both mobile and desktop, but worse overall, because most of its traffic came from mobile, which has lower conversion anyway. The mix of groups, not the treatment, drives the total. The lesson is to check segment-level results and confounding variables before drawing conclusions from aggregated numbers.
24
Why do ratio metrics like click-through rate per user need special variance calculation?
A metric like total clicks divided by total page views has a numerator and denominator that both vary by user, and page views within a user are correlated. Treating each page view as independent underestimates variance and inflates false positives. The delta method approximates the variance of a ratio of means using the variances and covariance of the per-user numerator and denominator. Bootstrap by user is an alternative when formulas get complicated.
25
A feature won its A/B test with a 4 percent lift, but after full launch the metric barely moved. Why might that happen?
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.
26
Write a Python function to flatten a nested list of any depth.
def flatten(items):
out = []
for x in items:
if isinstance(x, list):
out.extend(flatten(x))
else:
out.append(x)
return out
print(flatten([1, [2, [3, 4]], 5])) # [1, 2, 3, 4, 5]
Do not pick 3,000 at random. Label a small diverse seed set, using embedding clustering to cover variety, and train a first model. Then use active learning: label the images the model is least confident about or that disagree across models. Use semi-supervised methods like pseudo-labelling high-confidence images, or self-supervised pretraining on all 50,000. Keep a randomly sampled test set so evaluation stays unbiased.
It tries candidate splits and picks the one that most reduces impurity — Gini or entropy (information gain) for classification, variance / MSE for regression.
It models the log-odds of a binary outcome as a linear function and passes it through a sigmoid to give probabilities, trained by maximising likelihood.
31
Pandas: compute a 7-day rolling average of daily sales for each store.
Scheduled retraining, like monthly, is simple and predictable but may be too late after a sudden change or wasteful when nothing changed. Trigger-based retraining runs when monitoring shows drift or a performance drop, which reacts faster but needs reliable monitoring. Many teams combine both: a regular schedule plus triggers for big shifts. Either way, a retrained model must pass the same evaluation gate before replacing the current one.
33
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.
34
Describe a production incident with a model and how you handled it.
Situation: one morning our recommendation model started showing the same five products to all users. Task: restore service and find the cause. Action: I rolled back to the previous model version within 20 minutes, then traced the issue to an upstream table that had arrived empty, so all user features were defaults. Result: we added data volume checks before scoring and a blameless postmortem. Rollback readiness saved us that day.
35
You assign values to a filtered DataFrame and Pandas shows a SettingWithCopyWarning. What is going on and how do you fix it?
The warning appears when you assign values to something that may be a copy of a slice instead of the original DataFrame, for example df[df.age > 30]['flag'] = 1. The original may not change. Fix it by using a single .loc call, like df.loc[df.age > 30, 'flag'] = 1, or by taking an explicit .copy() when you really want a separate DataFrame. Copy-on-write in newer Pandas versions makes this behaviour more predictable.
36
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.
37
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.
38
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.
39
What is gradient descent and what does the learning rate do?
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.
40
You need forecasts for 50,000 SKUs every week. Would you build one model per series or one global model?
One classical model per SKU is simple and interpretable but expensive to maintain, and it fails for short or sparse series. A global model, such as LightGBM trained on all SKUs with lags, calendar and product features, learns shared patterns and handles new items better. In practice I would use a global model as the main engine, keep statistical models for a few high-value series, and compare both in a rolling backtest.
Tips to crack the MakeMyTrip Data Science interview
Prepare cases on travel funnels, such as drop-offs between search and booking or hotel conversion rates.
Practise testing scenarios for booking flows, including fare changes, payments, cancellations and refunds across flights and hotels.
Read about MakeMyTrip, Goibibo and redBus to understand its multi-brand travel business.
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; MakeMyTrip interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the MakeMyTrip Data Science interview?
Candidates usually go through 5 stages: Online assessment (coding or SQL); Technical round 1; Technical round 2 or case round; Hiring manager 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 MakeMyTrip Data Science interview?
This practice set leans on A/B Testing & Experimentation (6), MLOps and Model Deployment (6), Machine Learning (5), Deep Learning and NLP (5). Revise those first, then practise explaining one or two of your own projects end to end, because most MakeMyTrip interviewers ask you to walk through real work.
Are these the exact questions asked at MakeMyTrip?
No. These are curated practice questions that match the skills and difficulty usually tested in e-commerce & consumer tech interviews like MakeMyTrip's. They are not a leaked or verified list of past questions.
Where does MakeMyTrip hire Data Science professionals in India?
MakeMyTrip has large teams in Gurugram, Bengaluru. Openings and locations change often, so check the MakeMyTrip careers page and filter by city and experience.