Data Scientist Machine Learning Interview Questions
Practice 434 real Machine Learning interview questions for Data Scientist roles. From companies including Meta, Amazon, Google, Capital One, TikTok.
"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."
Explain SHAP in an ML System
Describe how you would build an end-to-end machine learning system for a business use case such as churn prediction, ad conversion prediction, or cont...
Solve Probability and Statistics Questions
This question evaluates proficiency in statistical modeling (ordinary least squares linear regression), probability theory (law of large numbers and c...
Daily Temperatures by Town: Linear Models to Predict NYC Temperature
Daily Temperatures by Town: Linear Models to Predict NYC Temperature You are given a dataset of historical daily temperature readings for New York Cit...
How to deploy and tune multimodal models?
Question You are interviewing for a new-grad machine learning / data scientist role at ByteDance. Answer the following related machine-learning and LL...
Sealed-Bid Auction for a Box of 200 Coin Flips (and an Informed Opponent)
You are one of two bidders in a sealed-bid auction for a box. The box's payout is determined by flipping 200 fair coins: it pays \$1 for every coin th...
Engineer and Impute ZIP Features
Question You are building a predictive model for a product team. For some users you have address fields such as street, city, state, and ZIP code. Man...
Explain variance reduction in random forests
This question evaluates understanding of variance reduction in ensemble methods, the impact of inter-tree correlation on averaged predictors, and the ...
Implement K-means and handle train-inference mismatch
Part A — K-means (implementation + concepts) You are given a dataset \(X \in \mathbb{R}^{n \times d}\) and an integer \(k\). 1. Explain K-means: what ...
Design and sample for credit default prediction
A bank wants a model to predict 90-day credit card default at account-month level for proactive outreach. Class prevalence in production is about 2% d...
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 ...
Evaluate Promotions for Uber Eats Users
This question evaluates a data scientist's causal inference and experimentation competencies—including randomized trial design, treatment-effect estim...
Handle imbalance, sampling, and overfitting
This question evaluates a data scientist's proficiency in machine learning topics including handling class imbalance, selecting and interpreting evalu...
Build cold-start restaurant ratings
This question evaluates a data scientist's ability to design a production-ready predictive modeling approach for cold-start ratings, testing competenc...
Perform no-intercept linear regression from two datasets
You are given two pandas datasets to fit an OLS model without an intercept (through origin). Dataset A (features): df_X(user_id, clicks, impressions)....
Model flight delays with EDA and explanation
Predicting 15+ Minute Arrival Delays at Scheduled-Departure Time You are building a binary classifier that predicts whether a domestic flight will arr...
Explain AUC, activations, ensembles, and imbalance
Machine Learning Metrics and Modeling Choices — Multi-part You are given model scores and binary labels for a small dataset and asked to compute ROC A...
Design and evaluate a RAG system
You are interviewing for an L5 Data Scientist role focused on LLM applications. Design a retrieval-augmented generation (RAG) system for an internal q...
How would you explain PCA and SHAP?
Question You are interviewing for a Data Scientist role at Point72, a systematic / quantitative investment firm. The interviewer asks you to pick one ...
Predict driver acceptance
This question evaluates a candidate's competency in designing and operationalizing an end-to-end machine learning solution for predicting driver accep...
Design a leak-free time-split model
Predict 30-Day Purchase Probability at a Snapshot (Technical Screen) Assume you have user, event, and order data with two timestamps per row: - event_...