Amazon Data Scientist Interview Questions
Amazon Data Scientist interview questions are famously comprehensive because Amazon evaluates both technical depth and Amazonian fit. Expect a mix of SQL and Python problems, statistics and experiment-design questions, machine‑learning discussion, and behavioral probes tied to Amazon’s Leadership Principles. Interviews typically include an initial recruiter screen, one or two technical phone screens, and a loop of 4–6 on‑site/virtual interviews where each 45–60 minute slot focuses on a different competency. Interviewers look for clear problem decomposition, metric-driven thinking, defensible trade‑offs, and the ability to translate analysis into business impact. For effective interview preparation, build a structured plan: craft concise STAR stories mapped to Leadership Principles with quantified outcomes, drill SQL (joins, window functions, CTEs, performance), refresh statistics and A/B testing fundamentals, and sharpen Python/data-manipulation skills. Practice explaining assumptions, communicating results for technical and non‑technical audiences, and walking through model choices and evaluation metrics. Mock interviews and timed problem sets that simulate the loop rhythm are especially valuable to convert knowledge into polished, confident answers.
"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 under disagreement
Behavioral: Disagreeing on a Launch Under Deadline (Data Scientist) You are a Data Scientist interviewing onsite for a behavioral and leadership round...
Demonstrate leadership under strict rules
Behavioral — STAR: Operating Under a Non‑Negotiable Policy Context: Onsite behavioral & leadership interview for a Data Scientist. Describe a specific...
Write and explain gradient descent pseudocode
Task: Batch Gradient Descent for Linear Regression (with Intercept) You are interviewing for a Data Scientist role and are asked to implement batch gr...
Demonstrate ownership and communication under pressure
Behavioral Interview: Ownership, Dive Deep, Raise the Bar (Data Scientist) Provide concise, data-backed stories using STAR (Situation, Task, Action, R...
Compare Random Forests vs Gradient Boosting rigorously
Technical ML Choice: Random Forest vs. Gradient-Boosted Trees for Large-Scale Binary Classification Problem Setup You need to choose between a Random ...
Design causal study for reminder impact
Observational Causal Study: Reminder Program With Staggered Market × Channel Launch Context You are evaluating the causal impact of medication-subscri...
Describe missed deadline and scope expansion
Behavioral Question: Accountability and Ownership for a Data Scientist You will be asked for two concrete, distinct examples: - (a) One time you misse...
Choose Between Fine-Tuning and RAG for Client Chatbot
Scenario You are building a client-facing chatbot that must answer questions grounded in the client's proprietary documents. You must choose how to im...
Solve Algorithmic Challenges in Online Coding Assessments
Scenario Online assessment requiring implementation of common algorithmic problems. Question Given an array of integers and a target, return indices o...
Design an ML Model for Interview Recommendation Pipeline
Scenario You are designing and deploying an ML model that mirrors a real-world recommendation pipeline serving a large product catalog with strict lat...
Design A/B Test for New Amazon Recommendation Module
A/B Test Design: Home Page Recommendation Module Scenario Amazon plans to introduce a new product recommendation module on the home page and wants to ...
Demonstrate Leadership in Challenging Situations and Decision-Making
Amazon Data Scientist Onsite — Bar-Raiser Behavioral Loop Context You will be asked behavioral questions that assess Amazon Leadership Principles (LPs...
Explain Central Limit Theorem and Its Limitations
Statistics Concepts and Disease-Test Evaluation Context You are assessing core statistical concepts used in evaluating diagnostic tests and in data sc...
Analyze User Engagement with SQL Queries
events +----------+---------+---------------------+ | event_id | user_id | event_time | +----------+---------+---------------------+ | 1 ...
Identify Top Spenders and Segment Customers Using Python
orders +----------+---------+------------+----------+--------------+-------------------+ | order_id | cust_id | order_date | product | order_amount |...
Demonstrate Leadership and Ownership in Energy Analytics Role
Behavioral Interview Prompt: Ownership, Resume Walkthrough, and Motivation Context Onsite behavioral & leadership interview for a Data Scientist role ...
Compute an A/B test p-value by hand
In an A/B test on a game feature, you measure conversion rate (binary outcome). - Control: n₁ = 1000 users, x₁ = 120 conversions - Treatment: n₂ = 980...
Evaluate Ensemble Models for Bias-Variance, Speed, and Interpretability
Large-Scale Recommendation System: Ensembles, Overfitting, Metrics, Architectures, and Optimization Context You are designing a large-scale recommenda...
Explain Overfitting and Underfitting in Machine Learning
ML Fundamentals and Computer Vision: Core Concepts Instructions You are interviewing for a data science role focused on classical ML and computer visi...
Deliver a Data Solution Under Tight Deadlines
Behavioral Prompt: Delivering Under a Tight Timeline Scenario A critical product launch date was moved up by two weeks, and your team is already at ca...