Amazon Data Scientist Machine Learning Interview Questions
Practice 37 real Machine Learning interview questions for Data Scientist roles at Amazon.
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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...
Design an end-to-end spam detection system
Design an End-to-End Email Spam Detection System You are asked to design a production-grade email spam detection system that meets the following const...
Optimize precision–recall under class imbalance
You have extreme class imbalance (positive rate ~1%). You score 12 examples as follows (id, true_label, score): A,1,0.92; B,0,0.90; C,0,0.88; D,0,0.70...
Apply Double ML with text-address features
Estimate the ATE of a First Reminder on CSAT via Double Machine Learning (DML) Context You have observational data on customer satisfaction (CSAT) sur...
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...
Diagnose and fix underperforming ML model
Rapidly Improving Recall Under Class Imbalance (One-Day Plan) Context You inherit a binary fraud detection model with severe class imbalance (positive...
Design fraud detection across channels with unknowns
Fraud Detection Strategy for a Multi‑Channel Marketplace Context: You are designing a fraud detection system for a large marketplace operating across ...
Derive and compare core ML and RL methods
ML Fundamentals Technical Screen — Multi‑part Question Context: You are given a set of core machine learning topics to address rigorously. For each pa...
Decide standardization, sparse numerics, correlated features
You are given a tabular dataset for supervised learning with features: F1 (counts, mostly small integers with many zeros), F2 (monetary amounts in dol...
Build a package-allocation model for couriers
Automatic Package-to-Courier Assignment with ML + Optimization You previously assigned packages to couriers manually. Design an end-to-end system that...
Explain core ML concepts and metrics
You are interviewing for a Data Scientist role. Answer the following ML fundamentals questions clearly and concisely. Concepts 1. Explain the bias–var...
Build Accurate Energy Consumption Prediction Model for Utilities
Predicting Daily Energy Consumption: End-to-End Regression to Production Context You need to build and productionize a supervised regression model tha...
Choose regularization norms and model formulations
Regularization and model choice. 1) For linear and logistic regression, write the objective functions with L0, L1, L2, and L-infinity penalties in bot...
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 ...
Optimize Email Strategy for New Prime Video Series Launch
Scenario Designing, deploying, and evaluating ranking models and marketing emails for Prime Video. Question How would you approach sending marketing e...
Design a Churn Model: Handle Missing Data and Justify
Churn Prediction on Messy Subscription Data Context You are building a binary churn-prediction model for a subscription product. Historical customer-l...
Explain Decision-Tree Training and Clustering Algorithms
Decision Trees and Clustering: Training Mechanics and Core Principles Context Technical/phone screen for an Applied Scientist/Data Scientist role, ass...
Explain random forests, bagging, and evaluation
Random Forests, Bagging vs Boosting, and Practical Model Validation You are building a supervised learning model on tabular data. Explain and compare ...
Implement Batch Gradient Descent for Linear Regression
Batch Gradient Descent for Linear Regression (MSE) Scenario You are building a linear regression model from scratch and will optimize its parameters u...
Compare RNNs and Transformers for Long-Sequence Text Classification
Scenario You are designing a long-sequence text classification system under tight inference latency constraints (e.g., large documents or logs that mu...