Data Science Interview Questions for Product Companies
40 questions weighted to the interview style of product companies — 32 medium, 8 hard.
How product companies interview for Data Science
Product companies run deeper interviews with fewer, harder questions. Expect hands-on problem solving, scenario and design questions, follow-ups that test your depth, and a round on ownership and impact. Your projects should show measurable results.
This set focuses on Statistics and Probability (5), Python for Data Science (5), Deep Learning and NLP (5), Feature Engineering (4).
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01
Your daily sales forecast badly misses the Diwali spike every year. How do you fix it?
The model is treating Diwali as noise because the festival moves on the calendar and a fixed yearly seasonality does not align. Add explicit holiday features: a flag for Diwali and a few days before and after, since buying starts early. Include other regional festivals and sale events too. Models like Prophet or gradient boosting with holiday features handle this well. Then backtest specifically on past festive periods to confirm the fix.
02
Write a Python function to split a long text into overlapping chunks by word count for a RAG index.
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.
05
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.
06
Write Python code that validates prediction requests with Pydantic before passing them to a model.
Effect size measures how large a difference is, for example the absolute lift in conversion or Cohen's d for means. A p-value only says whether the difference is likely due to chance. With millions of users, a 0.01 percent lift can be significant yet useless for the business. Reporting effect size with a confidence interval tells decision makers both whether the effect is real and whether it is worth acting on.
08
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;
09
Your fraud model's true labels arrive 30 days late. How do you monitor it in production?
Since accuracy cannot be measured quickly, monitor proxies. Track input data drift with PSI per feature, prediction score distribution, the share of transactions flagged, and data quality like missing values. Watch early signals such as chargeback complaints or manual review outcomes on a sample. Set alerts on sudden shifts. When labels arrive, compute real precision and recall by cohort and compare them with the proxy alerts to tune them.
10
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.
11
What are the novelty effect and the primacy effect in A/B tests?
The novelty effect is when users engage more with a change simply because it is new, so early results overstate the long-term lift. The primacy effect is the opposite: existing users resist change at first, so early results understate it. Both fade with time. To detect them, plot the treatment effect by day and by user tenure, compare new users with existing users, and run the test long enough for behaviour to settle.
12
Write Python code to apply a CUPED adjustment using each user's pre-experiment metric.
First pin the environment: a requirements.txt or conda environment file with exact versions, or a Docker image. Set random seeds for NumPy, scikit-learn and any framework you use. Remove hard-coded local paths and read them from a config file. Make sure data comes from a shared, versioned location. Finally, restart the kernel and run the notebook top to bottom, so hidden cell order bugs are caught before sharing.
15
How would you design an ML platform for a team of 20 data scientists?
I would start from the team's pain points, then provide shared building blocks: a standard project template, managed notebooks with access to the warehouse, experiment tracking and a model registry, an orchestrator like Airflow for pipelines, a feature store if real-time use cases exist, a standard serving path using containers, and default monitoring. Prefer managed services over custom builds, and add self-serve documentation so teams do not depend on one platform engineer.
Convolution filters slide over the input to detect local patterns, pooling reduces size, and deeper layers combine edges into shapes and objects. Used mainly for images.
17
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.
18
Write NumPy code for non-maximum suppression on a set of boxes and scores.
The knowledge index probably still holds old document versions, or the model is answering from its training data instead of retrieved context. Fix the ingestion pipeline to re-index on document change and remove superseded versions. Store effective dates in metadata and filter or boost the latest. Instruct the model to answer only from provided context and show the source with its date, so users can verify it.
20
Write Python code to compute a 95% bootstrap confidence interval for the mean.
Each test at 5 percent significance has a 5 percent false positive chance. Run 20 independent tests and you expect about one false positive even if nothing is real. Corrections include Bonferroni, which divides alpha by the number of tests and is strict, and Benjamini-Hochberg, which controls the false discovery rate and keeps more power. Also reduce the problem by choosing one primary metric before the experiment starts.
22
Write Python code to compare revenue per user between two groups using Welch's t-test.
It computes the gradient of the loss with respect to every weight by applying the chain rule backwards through the network, so gradient descent can update them.
25
What is the difference between hard voting and soft voting in ensembles?
In hard voting each model predicts a class and the majority wins. In soft voting each model gives class probabilities, they are averaged, and the class with the highest average wins. Soft voting usually works better because it uses each model's confidence, but it needs calibrated probabilities from all models. Hard voting is simpler and works even with models that only output labels.
26
Write a scikit-learn ColumnTransformer that preprocesses numeric and categorical columns.
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.
28
Write SQL to find customers who ordered in January 2026 but not in February 2026.
SELECT DISTINCT o.customer_id
FROM orders o
WHERE o.order_date >= '2026-01-01' AND o.order_date < '2026-02-01'
AND NOT EXISTS (
SELECT 1 FROM orders f
WHERE f.customer_id = o.customer_id
AND f.order_date >= '2026-02-01' AND f.order_date < '2026-03-01'
);
29
You need to use a device_id column with millions of unique values in a streaming model. How would feature hashing help?
Feature hashing, the hashing trick, maps categories or tokens into a fixed number of columns using a hash function, without storing a vocabulary. It handles very high-cardinality or ever-changing values, like URLs or device IDs, with constant memory and works well in streaming systems. The trade-off is collisions, where different values share a column, and the features cannot be reversed for explanation. Choose a large enough number of buckets.
30
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
31
Write a prompt template for classifying customer support tickets.
System: You classify support tickets for an e-commerce company. Categories: payment, delivery, refund, account, other. Rules: choose exactly one category; if unsure, choose other; do not invent details. Return JSON with fields category and reason, reason under 20 words. Then two short examples showing a ticket and the expected JSON. User: Ticket: {ticket_text}. Keep the template fixed and test it on 100 labelled tickets before rollout.
32
Your sales forecast scores well in k-fold cross-validation but fails in production. What could be wrong with the validation?
Random k-fold mixes future and past rows, so the model trains on data from after the period it predicts. That leaks information and inflates scores. Use time-based splits instead: train on an expanding or rolling window and validate on the next period, like TimeSeriesSplit in scikit-learn. Leave a gap between train and validation when features use lagged values, so the test reflects real forecasting conditions.
33
What is U-Net and why is it popular for segmentation?
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.
34
A key metric dropped 8 percent week over week. How do you check whether it is real?
First rule out data issues: tracking changes, pipeline delays, missing partitions or a changed metric definition. Then compare with the same week last year and look for holidays or seasonality. Check normal weekly variation to see if 8 percent is unusual. Break the drop down by platform, region, app version and user type to find where it is concentrated. Finally, check releases, campaigns or outages that happened that week.
35
What is the difference between iterators and iterables in Python?
An iterable can produce an iterator (it has __iter__); an iterator has __next__ and remembers its position. A for-loop calls iter() on the iterable and then next() repeatedly.
36
Marketing wants to measure the impact of a TV and outdoor campaign that cannot be randomised at user level. How do you design the measurement?
Plan a geo experiment: select comparable cities or regions, randomly assign some to run the campaign and keep others as control, matching them on past sales trends. Measure the difference in sales during and after the campaign, using methods like difference-in-differences or synthetic control to build a counterfactual. Run a pre-period check that test and control regions track each other, and include enough regions for reasonable power.
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.
38
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.
39
How would you structure a data science codebase so notebooks do not become unmaintainable?
Keep notebooks for exploration and reporting only. Move reusable logic into a Python package with modules for data loading, features, training and evaluation, and import it into notebooks. Keep settings in config files, write unit tests for feature functions, and use pre-commit with a formatter and linter. Track experiments in a tool like MLflow. A clear folder template and code reviews keep the team consistent as the project grows.
40
Pandas: find customers whose monthly spend increased for three consecutive months.
How are Data Science interviews at product companies different?
Product companies run deeper interviews with fewer, harder questions. Expect hands-on problem solving, scenario and design questions, follow-ups that test your depth, and a round on ownership and impact. Your projects should show measurable results.
Which product companies are covered?
This page links to 31 product companies, including Amazon, Google, Meta, Microsoft, Adobe, Oracle, Salesforce, ServiceNow. Each has its own page with rounds and a separate practice set.
What rounds should I expect?
Common rounds at these companies include: Hiring manager round; Online coding assessment; Technical round 1 (DSA); HR round; Technical round 1. The exact process varies by company and role.