Zerodha Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Zerodha hiring for Data Science roles
Zerodha is an Indian stockbroker known for discount brokerage and platforms like Kite, Console and Coin. It runs a relatively small in-house technology team that builds trading and back-office systems. Hiring for technical and analytics roles is limited and tends to value practical skills, open-source work and self-learning.
For Data Science roles, this Zerodha practice set focuses most on A/B Testing & Experimentation (7), MLOps and Model Deployment (5), Machine Learning (5), Python for Data Science (5). It has 2 easy, 32 medium, 6 hard questions, so you can start easy and work up to the harder ones.
Company type
Fintech
Headquarters
Bengaluru, India
Main India offices
Bengaluru
Zerodha Data Science interview process
Application review (projects or GitHub)
Technical discussion
Assignment or practical task
Founder or team round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for ZerodhaStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
✓ 3 questions free • Unlock the rest in seconds
01
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
02
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;
03
Your power analysis says you need 200,000 users per group, but the page gets only 5,000 users a week. What are your options?
Waiting 80 weeks is unrealistic. Options: test a bolder change so the expected effect is larger; use a more sensitive metric closer to the change, like add-to-cart instead of purchase; apply CUPED to reduce variance; or move the test to a higher-traffic page. If none work, accept a larger MDE explicitly, or decide using qualitative research and a staged rollout with monitoring instead of a formal test.
04
Your deployed model gives different predictions than the same model in your notebook. Why?
Usually preprocessing differs between training and serving. Check that the same pipeline object was saved, not just the estimator, and that category order, missing value handling and scaling match. Compare library versions, feature types like strings versus integers, column order, and time zones in date features. Send the same few rows through both paths and compare features step by step to find the first mismatch.
05
Tell me about a time you automated a manual step in a machine learning workflow.
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.
During training it randomly switches off a fraction of neurons so the network cannot depend on any single unit, acting as a regulariser against overfitting.
ARIMA models a time series using three parts. AR, autoregressive, uses past values of the series. I, integrated, differences the series d times to remove trend and make it stationary. MA, moving average, uses past forecast errors. You choose p, d and q using ACF and PACF plots or information criteria like AIC. SARIMA adds seasonal terms. ARIMA works well for single, fairly stable series but struggles with many external drivers.
08
You want to test a new pricing algorithm in a two-sided marketplace where buyers and sellers interact. Why is user-level randomisation a problem and what would you do?
Treated and control buyers compete for the same sellers' supply, so a treatment that makes treated buyers buy more takes inventory from control buyers, exaggerating the measured lift. Use designs that limit interference: switchback tests that alternate the whole market between variants over time slots, or randomise by city or region. Analyse at the switch or cluster level with appropriate variance, and accept that these tests need longer to reach power.
09
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.
10
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.
11
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.
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.
13
Training a transformer on a GPU fails with an out-of-memory error. What can you do?
Reduce the batch size and use gradient accumulation to keep the same effective batch. Shorten the maximum sequence length. Turn on mixed precision training with float16 or bfloat16. Use gradient checkpointing, which saves memory by recomputing activations. Freeze some layers or switch to a smaller model, or use parameter-efficient fine-tuning like LoRA. Also make sure no old tensors stay on the GPU between runs.
14
A 10 million row DataFrame with a city column is using too much memory. How can the category dtype help?
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.
15
Your test shows a 2 percent lift in conversion, but revenue per user went down. What do you do?
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.
16
Most of your products sell zero units on most days, with occasional sales. Which forecasting approach suits this?
This is intermittent demand, and standard models like ARIMA forecast a small constant that is useless for stocking. Croston's method and its SBA variant forecast the size of non-zero demand and the interval between sales separately. Alternatives are aggregating to weekly level, or using a global gradient boosting model with Tweedie or Poisson loss. Evaluate with MAE or MASE, not MAPE, because zero actuals break percentage errors.
17
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
18
What is the difference between batch and real-time inference?
Batch inference scores many records on a schedule, for example nightly churn scores for all customers written to a table. It is cheap and simple. Real-time inference serves predictions on request through an API within milliseconds, for example fraud checks during payment. It needs low-latency feature access, scaling and high availability. Choose real-time only when the decision truly must use fresh data at the moment of the request.
19
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.
20
Your neural network's training loss suddenly becomes NaN. What do you check?
Common causes are a learning rate that is too high, exploding gradients, bad input values, and unstable maths like log of zero. I would check the input data for NaN or infinite values, lower the learning rate, add gradient clipping, and make sure the loss uses the stable built-in version with logits. With mixed precision, I would check the loss scaler. Logging gradient norms per step helps find the exact point it breaks.
21
Your SQL result does not match the number on the official dashboard. How do you resolve it?
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.
22
How would you build RFM features for a customer churn model?
RFM stands for recency, frequency and monetary value. From the transactions table, for each customer as of a cut-off date: recency is days since the last purchase, frequency is the number of orders in the last 90 days, and monetary is total or average spend in that window. Compute them using only data before the cut-off, add trends like spend this month versus last, and the label from the period after.
As the number of independent trials grows, the sample average converges to the true expected value, which is why larger samples give more stable estimates.
24
Write Python code to fit a Holt-Winters model and forecast the next 12 months.
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.
26
Your model performance degrades every few months. Would you retrain on a fixed schedule or based on triggers?
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.
They store same-type values in contiguous memory and run vectorised operations in compiled C, avoiding Python loop overhead and per-object type checks.
28
Tell me about a feature you engineered that significantly improved a model.
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
Write SQL to find users who logged in on at least three consecutive days.
WITH d AS (
SELECT DISTINCT user_id, CAST(login_at AS DATE) AS day
FROM logins
),
g AS (
SELECT user_id, day,
day - CAST(ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY day) AS INT) AS grp
FROM d
)
SELECT DISTINCT user_id
FROM g
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;
-- date minus row number is constant within a streak (PostgreSQL syntax)
*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.
31
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.
32
Halfway through a test you discover a bug that broke the treatment for Android users. What do you do?
Stop or pause the affected part, fix the bug and restart the test with fresh data, since mixing broken and fixed periods biases results. Do not simply drop Android users from analysis afterwards, because that can break randomisation balance if the bug affected behaviour or logging. Document the incident, and add pre-launch checks such as running the variant through QA on every platform and monitoring guardrails in the first hours.
33
Your RAG chatbot often answers using the wrong documents. How do you fix it?
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.
It predicts from the k nearest labelled points. It is simple but slow at prediction time, sensitive to scaling and irrelevant features, and weak in high dimensions.
35
Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.
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.
Simple exponential smoothing forecasts with a weighted average of past values where weights decay exponentially, so recent points matter more. Holt's method adds a smoothed trend component. Holt-Winters adds a seasonal component as well, either additive or multiplicative. Each component has a smoothing parameter between 0 and 1 that is fitted to the data. These models are fast, easy to explain and strong baselines for retail and demand data.
39
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.
40
Write Python code to apply a CUPED adjustment using each user's pre-experiment metric.
import numpy as np
# df has: variant, y (metric during test), x (same metric pre-test)
x, y = df['x'].to_numpy(), df['y'].to_numpy()
theta = np.cov(x, y, ddof=1)[0, 1] / np.var(x, ddof=1)
df['y_cuped'] = y - theta * (x - x.mean())
print(df.groupby('variant')[['y', 'y_cuped']].agg(['mean', 'var']))
# Means stay comparable; variance of y_cuped is lower
Tips to crack the Zerodha Data Science interview
Build and share real projects on GitHub, as Zerodha is known to value practical work and open-source contributions.
Read Zerodha Varsity to understand trading, markets and investment concepts in depth.
Learn about Zerodha's tech stack and engineering blog, including its use of Go and open-source tools.
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; Zerodha interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Zerodha Data Science interview?
Candidates usually go through 4 stages: Application review (projects or GitHub); Technical discussion; Assignment or practical task; Founder or team 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 Zerodha Data Science interview?
This practice set leans on A/B Testing & Experimentation (7), MLOps and Model Deployment (5), Machine Learning (5), Python for Data Science (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Zerodha interviewers ask you to walk through real work.
Are these the exact questions asked at Zerodha?
No. These are curated practice questions that match the skills and difficulty usually tested in fintech interviews like Zerodha's. They are not a leaked or verified list of past questions.
Where does Zerodha hire Data Science professionals in India?
Zerodha has large teams in Bengaluru. Openings and locations change often, so check the Zerodha careers page and filter by city and experience.