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."
Estimate Revenue and Profitability for Share Workplace's Paid Tier
Estimate Revenue and Profitability for Share Workplace's Paid Tier Freemium + Subscription Sizing, Margins, and Marketing Impact (Year 3) Context You ...
Evaluate K-Fold Cross-Validation for Model Selection
Evaluate K-Fold Cross-Validation for Model Selection Model Selection and Validation for a New Feature Launch You are selecting and validating predicti...
Design a Churn Model: Handle Missing Data and Justify
Design a Churn Model: Handle Missing Data and Justify Churn Prediction on Messy Subscription Data Context You are building a binary churn-prediction m...
Describe Handling Unexpected Feedback and Actions Taken
Describe Handling Unexpected Feedback and Actions Taken Behavioral: Resilience After Unexpected Negative Feedback or Rejection Context You are in an o...
Compute answers to probability and counting puzzles
Compute answers to probability and counting puzzles Below are several independent interview-style math/logic questions. 1) Barn legs You see only spid...
Diagnose KPI anomaly and evaluate promotion/A-B test
Diagnose KPI anomaly and evaluate promotion/A-B test You are a Data Scientist supporting a TurboTax product team. You are asked to handle three relate...
Build a predictive model from TurboTax sample data
Build a predictive model from TurboTax sample data You receive a TurboTax sample dataset (user-level and/or session-level) and are asked to build a pr...
Handle multicollinearity in feature selection
You are building an interpretable predictive model for an insurance company, such as a linear or logistic regression model for claim risk. Several inp...
Answer core behavioral questions for data roles
Answer core behavioral questions for data roles You are interviewing directly with a hiring manager who is known to be very selective. The interview i...
Implement sampling and subarray algorithms
This coding round contained two algorithmic prompts: uniform sampling in a 2D square and longest increasing contiguous subarray. Constraints & Assumpt...
Describe initiative, project, analysis, and relationship-building
Behavioral and Leadership Questions — Data Scientist Technical Screen Context You are interviewing for a Data Scientist role and will be asked to demo...
Determine roles from B's accusation
Knights and Knaves: Single Statement Inference You meet two islanders, A and B. Each person is either a knight (always tells the truth) or a knave (al...
Implement drawdown, single-trade profit, coin change
Implement and analyze the following three problems: 1) Maximum drawdown of cumulative PnL: "What is the maximum drawdown of a cumulative PnL (profit a...
Compute odds under time pressure
Timed Probability: At Least Two Reds in Three Draws You have an urn with r red, b blue, and g green balls. Let N = r + b + g with r, b, g ≥ 0 and N ≥ ...
Detect earliest collision among moving cars
You are given n vehicles with kinematics parameters. A “collision” means two vehicles occupy the same position at the same time. Assume continuous tim...
Explain multicollinearity and OLS assumptions
Explain multicollinearity and OLS assumptions Linear Regression Technical Screen: OLS Assumptions and Multicollinearity Context: You are asked to summ...
Walk through a DS project end-to-end
Prompt Describe one data science / analytics project you worked on, end-to-end. What to cover Include concise but concrete details on: - Problem & goa...
Relate coefficients under linear feature transformation
Suppose you are fitting a linear regression model and you consider two different feature parameterizations. Original features: x1, x2. Transformed fea...
Find top-5 most similar rows across datasets
You are given two datasets with the same feature columns: - source (rows you want to match): - source_id (STRING/INT) - f1...fk (NUMERIC; may cont...
Solve queue switch and coin change puzzles
This entry contains two separate interview-style quantitative/algorithmic questions. --- 1. Queue switching decision problem You are standing in a sin...