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."
Design A/B Tests for Banner Ad and Group-Story Feature
Design A/B Tests for a Banner Ad and a Group-Story Feature You are evaluating two product decisions in a consumer social app: adding a new banner ad p...
Design Scalable Database and Analyze E-commerce Data
transactions +-----------+----------+------------+------------+ | user_id | order_id | product_id | order_time | +-----------+----------+-----------...
Build Classifier: Evaluate with AUROC for Imbalanced Data
Detecting Dead Links: Build and Evaluate a Classifier You have a dataset of 1,000 URLs labeled as good, meaning alive, or bad, meaning dead. The class...
Implement Left Join Using Python Dictionaries Efficiently
Orders +---------+----------+--------+ | order_id| customer | amount | +---------+----------+--------+ | 101 | C1 | 250 | | 102 | ...
Calculate Posterior Probability of Flagged User Being Bad Actor
Calculate Posterior Probability of a Flagged User Being a Bad Actor A platform runs a binary classifier that flags users who might be bad actors. You ...
Determining the optimal ad load in News Feed
Determining the Optimal Ad Load in News Feed You are asked to set a data-driven threshold for ad frequency, where ad load means the number of ads show...
Explain Bias-Variance Trade-off Simply for Stakeholders
Explain the Bias-Variance Trade-off Simply for Stakeholders You are in a data scientist interview and need to explain a core modeling concept to a non...
Address Fraud Detection with Imbalance and Concept Drift Solutions
Address Fraud Detection with Imbalance and Concept Drift Solutions You are building a fraud-detection model for an online payments product that must s...
Analyze Hashtag Follow Behavior with SQL Queries
following_behavior +------------+---------+-----------+---------------+ | date | user_id | hashtag_id| hashtag_source| +------------+---------+-...
Design SQL Query for Shop Visibility and User Activity Metrics
SHOP_VISIBILITY +----------+---------+------------+------------+-------------+--------------+ | user_id | shop_id | event_date | is_visible | signup_...
Determine Posterior Probability of Bad User Prediction
Posterior Probability for a Bad-Actor Classifier You are evaluating a binary classifier that flags bad actors among users. Given: - 5% of users are tr...
Calculate Probabilities for Mixed Reviewer Types
Probabilities for Mixed Reviewer Types Two types of reviewers exist in a marketplace: - Lazy reviewers are 20% of reviewers and always give good revie...
Determine Metrics to Evaluate Notification Impact on Users
Determine Metrics to Evaluate Notification Impact on Users Facebook sends several types of push notifications and is considering a new notification th...
Convince Product Manager to Launch 'Show Similar Products' Button
Convince a PM to Test a "Show Similar Products" Button Instagram is considering adding a "Show similar products" button on product-tagged content to b...
Describe Overcoming a Major Challenge in Your Career
Describe Overcoming a Major Challenge in Your Career This is a behavioral deep-dive for a new-grad data scientist role. The interviewer may ask severa...
Evaluate Social Media's Brand Advertising Effectiveness
Evaluate Social Media's Brand Advertising Effectiveness A retailer runs both direct-response ads and brand-awareness ads. Leadership suspects social-m...
Define Credit and Its Importance for Consumers and Banks
Explain Credit and Why It Matters A bank is onboarding a new analyst and wants to confirm their understanding of fundamental lending concepts. You are...
Design Real-Time Credit Card Fraud Detection System
Design a Real-Time Credit-Card Fraud Detection System You are designing a real-time fraud detection system for an online payments platform that proces...
Resolve Conflicts and Deliver Results Under Pressure
Behavioral Interview: Conflict, Limited Resources, and Critical Feedback You are in cross-functional and hiring-manager interviews for a Data Scientis...
Identify User Interest in Group Video Calls Using Data
Identify User Interest in Group Video Calls Using Data You are designing and analyzing a new group video-calling feature for a large social or messagi...