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."
Demonstrate leadership, innovation, and learning via STAR
Behavioral & Leadership (Data Scientist, HR Screen) Instructions Answer each prompt concisely using the STAR format (Situation, Task, Action, Result)....
Explain and resolve Simpson’s paradox
Define Simpson’s paradox and construct a concrete numeric example where group-wise success rates favor treatment in each subgroup but the aggregate ra...
Implement a high-throughput web crawler safely
Design and code (pseudocode acceptable) a multi-threaded web crawler that favors breadth-first discovery while continuously running analysis tasks on ...
Detect sessions and gaps using SQL LEAD
Write a single ANSI-SQL query that (a) assigns per-user session_ids when the gap between consecutive events exceeds 30 minutes, (b) computes session_s...
Implement lazy unique-merge generator for sorted streams
Write a Python generator merge_unique(a, b) that lazily merges two nondecreasing iterables a and b (potentially infinite) into a single nondecreasing ...
Handle highly imbalanced classification data
You must build a binary classifier for fraud with a 0.2% positive rate and 10M rows × 500 features. Propose an end-to-end plan that covers: 1) data sp...
Diagnose and experiment to reduce late deliveries
Two-Sided Delivery Platform: Rising Late Deliveries You are the first analyst on a two‑sided delivery platform that handles both food and parcel order...
Design and diagnose a regression pipeline
CLV_90 Prediction Pipeline under Zero-Inflation, Heavy Tails, and Multicollinearity Context You need to predict 90-day customer value (CLV_90) at the ...
Design a switchback and choose block length
Switchback Experiment Design: Airport Pickup Pricing with Spillovers Context You are designing a switchback (time-based A/B) experiment for airport pi...
Find list pair with maximum overlap
You are given N labeled lists of items as a Python dict mapping list_name -> iterable of strings. Example input: {'L1': ['A','B','C'], 'L2': ['A','C',...
Test conversion difference and adjust for clustering
Using aggregated results for the 7‑day window 2025‑08‑26..2025‑09‑01, evaluate statistical significance and power for conversion uplift, accounting fo...
Build a regularized regression pipeline
Technical Screen: End‑to‑End Signup Prediction with scikit‑learn Context You are given a cleaned tabular dataset with marketing and product metrics. Y...
Describe handling pressure and stakeholder conflicts
Behavioral/Scenario Questions for a Data Scientist — Technical Screen Answer concisely using STAR (Situation, Task, Action, Result) where relevant. 1....
Handle an irate passenger after flight delay
Role-Play: Irregular Operations Recovery and Communication Plan Scenario - A flight is delayed 5 hours due to a crew time-out cascading from weather a...
Explain power drivers and resolve unexpected A/B results
A/B Testing: Power, Sample Size, Allocation, and Diagnostics You are analyzing a two-proportion (binary conversion) A/B test with independent users, n...
Process real-time enter/exit events and actives
You receive a real-time stream of events with schema: user_id (str), channel (str), event_type ("enter"|"exit"), ts (UTC ISO timestamp). A user can ‘e...
Explain SHAP vs VIF under collinearity
High Collinearity in Binary Classification: VIF, SHAP, and Interpretation Strategy You are modeling a binary outcome Y. Two numeric features A and B a...
Evaluate a model and choose metrics
Fraud-screening model evaluation under class imbalance and asymmetric costs Context You operate a binary classifier that flags e‑commerce orders for m...
Reverse a list in-place
Coding Task: Reverse a List In-Place (Python) Context You're implementing a utility function during a technical screen. The function must reverse a Py...
How to experiment on ETA reduction
Context You work on a consumer app where users see an ETA (estimated time to arrival/delivery) during a funnel (e.g., browsing → checkout → order plac...