Data Scientist Interview Questions
Practice 2,964 real Data Scientist interview questions for 2026. Data Scientist interview questions drawn from Meta, Capital One, Amazon, Google, TikTok and similar employers — real questions from actual interviews with detailed solutions — designed to accelerate your interview preparation for product analytics, ML and production data roles. This collection emphasizes the practical skills interviewers test: SQL and data manipulation, experiment design and A/B testing, statistical reasoning, Python coding for data problems, model evaluation and feature engineering, plus machine-learning system tradeoffs and metric design. What’s distinctive about modern data-science loops is the blend of product thinking and reproducible ML: expect hands-on SQL tasks and funnel analysis in screens, deeper experiment-design and causality questions in mid rounds, and coding or modeling challenges plus ML-system discussions in senior loops. Interviewers evaluate problem framing, statistical rigor, and how you communicate decisions to product partners. To prepare, prioritize daily SQL practice (CTEs, window functions), refresh hypothesis-testing and power calculations, rehearse concise metric-driven narratives, and build a few end-to-end model or experiment stories you can explain clearly under time pressure.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Test classifier difference with McNemar's test
Paired Comparison of Two Classifiers via McNemar's Test You evaluated two classifiers, A and B, on the same 10,000 labeled examples. Because both mode...
Describe leading an ambiguous ML project end-to-end
Behavioral & Leadership: End-to-End ML Project Under Ambiguity (STAR) Provide a STAR-format example where you led an end-to-end ML project with ambigu...
Resolve exclusion, learn fast, and manage conflict
Behavioral & Leadership Onsite — Cross-Team Inclusion, Fast Learning, Analytical Conflict Context You are a data scientist working cross-functionally ...
Compare Tableau blending, joins, and filters
Tableau Integration Exercise: Joins vs Blends, Filters, and Order of Operations Data You have two sources. Primary (Orders) - Columns: OrderID, State,...
Compare bagging vs boosting on imbalanced data
Fraud Detection on 10M Time-Ordered Transactions (0.5% Fraud) You are building a binary classifier to detect 0.5% fraudulent events among 10,000,000 t...
Manage promotions and project portfolio tradeoffs
Context You manage a 10-person Data Science team operating across multiple locations and time zones. Three senior individual contributors (ICs) are ac...
Model comment counts and detect anomalies
Modeling Heavy-Tailed Comment Counts and Robust Monitoring You are analyzing daily comment counts at the post–day level. The distribution is heavy-tai...
Design a feed ads A/B test with guardrails
Experiment Design: Insert One Extra Ad Every 8 Organic Posts in Main Feed Context You want to increase ad load by inserting one additional ad for ever...
Design a data platform enablement
Design a pragmatic data‑platform enablement for a mid‑tier retail bank migrating to cloud under PII and data‑residency constraints. Describe the targe...
Design and analyze notification pinning experiment
Experiment Design: Pinning Accounts With Active Notifications in the Account Switcher Context You are evaluating a UI feature that pins accounts with ...
Design Messenger spam experiment with clustering
Experiment Design: Spam-Detection Algorithm for Messenger You are evaluating a new spam-detection algorithm that routes suspected spam into a separate...
Derive logistic regression objective and gradients
Context: Binary Logistic Regression You are given a binary classification dataset {(x_i, y_i)}_{i=1}^m with labels y_i ∈ {0, 1}. The model uses the si...
Diagnose and fix selection bias in experiments
Selection Bias With Opt-in Discount Banner Context You observe that users can opt in to see a discount banner (treatment = 1 if they opted in and saw ...
Merge words by head-tail chaining
Given a list of lowercase words, merge them into a single string by repeatedly chaining words where the last character of the current string equals th...
Apply PSM rigorously for observational A/B analysis
Task: Estimate ATT on 7-Day Retention Using Propensity Score Matching (PSM) Context You are given observational, user-level product data where users s...
Design causal measurement without randomization
Causal Study Design: Notification Feature Impact on 7-Day Retention Context: A new notification feature shipped on 2025-06-01. Randomized rollout was ...
Implement stream random sampling in Python
You are given an unbounded stream of items that cannot be stored entirely in memory. Write Python code to maintain a uniform random sample from the st...
Explain Your Motivation and Alignment with Apple Values
Explain Your Motivation and Alignment with Apple Values Behavioral Interview — Motivation and Values (Apple, Data Scientist) Prompt Why do you want to...
Define Success with Contact Syncing for Growth and Evaluation
Define Success with Contact Syncing for Growth and Evaluation Using "% of users with contacts synced" as a growth driver Context You are a data scient...
Calculate Profitability and Break-Even for Lyft Partnership Campaign
Calculate Profitability and Break-Even for Lyft Partnership Campaign Credit Card Unit Economics and Lyft Co‑Marketing Break‑Even Context You are evalu...