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."
Reduce overfitting under constraints
Reduce Overfitting Under Latency Constraints (Tabular Regression) Context (assumed) - You have a tabular regression model with a large generalization ...
Design an ad-selection system across objectives
End-to-End Ad-Selection System Design Context You must choose, at impression time, which advertiser type to show to a user. There are three advertiser...
Evaluate and monitor a credit risk model
Credit-Risk PD Model: Evaluation Priorities and End-to-End Plan Context: You are deploying a consumer credit probability-of-default (PD) model for 12-...
Design real-time payments fraud model under constraints
Real-Time ML Policy Design: Prevent Unauthorized Purchases by Minors Context: You need to reduce unauthorized purchases by minors using their parents'...
Design a fintech product ranking system
Build a Personalized Ranking System for Financial Products (App Home Page) Context You are the first Data Scientist partnering with a PM to build an e...
Tune fraud threshold under review capacity and costs
Fraud Triage Thresholding with Calibrated Scores Context You have a fraud model that outputs a calibrated score s ∈ [0, 1] per account, where s ≈ P(fa...
Build ETA prediction and simulate impact
Predicting Delivery ETA (Minutes) Context You are given a take-home dataset with order-, store-, and dasher-level features. The goal is to predict del...
Diagnose overfitting, DenseNet, preprocessing, CV
ML Interview Task: Overfitting, DenseNet vs. ResNet, Medical Imaging Pipeline, Hyperparameter Tuning, and Cross-Validation 1) Overfitting - Define ove...
Design a fintech homepage ranker
Personalized Product Ranking for a Fintech Home Page — End-to-End Design Context You are designing a personalized ranking system for a fintech app’s h...
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...
Design a model for imbalanced conversions
Predicting Purchase Propensity After a Campaign (5% Positives) You previously ran a marketing campaign to 10,000 customers and observed 500 purchases ...
Explain factor leakage checks and IC/ICIR filtering
This question evaluates competency in factor-based predictive modeling, including detection of information leakage, use of information coefficient (IC...
Design a short-video recommender system
This question evaluates a data scientist's competency in end-to-end machine learning system design for recommender systems, including retrieval and ra...
Evaluate Product-Ranking Algorithm with Precision and Recall Metrics
Evaluate Product-Ranking Algorithm with Precision and Recall Metrics Scenario Instagram Shopping wants to improve its product‑ranking algorithm for th...
Optimize Surge Notifications for Rideshare Drivers
Optimize Surge Notifications for Rideshare Drivers Scenario A rideshare marketplace experiences airport demand spikes. When demand exceeds supply, the...
Identify Fake Accounts Using Machine Learning Techniques
Identify Fake Accounts Using Machine Learning Techniques Scenario You are a data scientist at Meta. Fake accounts (bots, spam, scams, impersonation, c...
Design an ML Model for Interview Recommendation Pipeline
Design an ML Model for Interview Recommendation Pipeline Scenario You are designing and deploying an ML model that mirrors a real-world recommendation...
Optimize XGBoost for Predicting Marketing Outcomes
Optimize XGBoost for Predicting Marketing Outcomes Gradient-Boosted Trees for Marketing Outcome Prediction Context You’re building a model to predict ...
Design a Machine Learning Recommendation System Pipeline
Design a Machine Learning Recommendation System Pipeline System Design: End-to-End ML Recommendation System Scenario You are building an end-to-end ma...
How would you predict a car’s turning intention?
At an intersection, there are n vehicles stopped or approaching. For each vehicle, you have a short history (e.g., last 3–10 seconds at 10 Hz) of: - P...