Amazon Data Scientist Interview Questions (2026)

Landing a Data Scientist role at Amazon requires targeted preparation. Amazon uses a structured interview process called the Bar Raiser loop. Each interviewer is assigned specific Leadership Principles to evaluate. Expect deep behavioral questions using the STAR method, combined with technical assessments relevant to the role. The Bar Raiser interviewer ensures hiring standards remain high across the company. This guide covers the most frequently asked questions and insider tips to help you succeed in your Amazon Data Scientist interview.

About the Amazon Interview Process

Amazon evaluates candidates heavily against their 16 Leadership Principles, making behavioral questions a critical part of every interview loop.

Amazon uses a structured interview process called the Bar Raiser loop. Each interviewer is assigned specific Leadership Principles to evaluate. Expect deep behavioral questions using the STAR method, combined with technical assessments relevant to the role. The Bar Raiser interviewer ensures hiring standards remain high across the company.

Why Amazon Data Scientist Interviews Are Different

Amazon Data Scientist interviews differ from standard Data Scientist interviews in several key ways. The company has a unique interview culture, specific evaluation criteria, and expects candidates to demonstrate alignment with their values and mission. Understanding these differences gives you a significant advantage over other candidates.

Top 10 Data Scientist Interview Questions at Amazon

1. At Amazon, you might be asked: Explain the bias-variance tradeoff.
2. At Amazon, you might be asked: How do you handle missing data in a dataset?
3. Expect this at Amazon: What is the difference between supervised and unsupervised learning?
4. Amazon interviewers often ask: Describe the steps you take in a typical data science project.
5. Amazon candidates should prepare for: How do you evaluate the performance of a classification model?
6. A common Amazon interview question: Explain regularization and when you would use it.
7. A common Amazon interview question: What is cross-validation and why is it important?
8. Amazon candidates should prepare for: How do you communicate complex findings to non-technical stakeholders?
9. Amazon interviewers often ask: Describe a project where your analysis led to a significant business decision.
10. At Amazon, you might be asked: What is the difference between correlation and causation?

Amazon-Specific Preparation Tips for Data Scientist Candidates

• Memorize and prepare stories for all 16 Amazon Leadership Principles
• Use the STAR method rigorously with specific metrics and outcomes
• Prepare multiple stories for Customer Obsession and Ownership principles
• Practice system design for scalable distributed services
• Be ready to discuss trade-offs and how you handle ambiguity and conflict

General Data Scientist Interview Tips

• Brush up on statistics and probability fundamentals
• Practice coding in Python or R with real datasets
• Prepare to explain complex models in simple terms
• Have portfolio projects that demonstrate end-to-end data science work

Preparation Timeline for Amazon Data Scientist Interviews

• 4 weeks before: Research Amazon culture, recent news, and the specific team you are applying to.
• 2-3 weeks before: Practice technical questions daily and prepare behavioral stories using the STAR method.
• 1 week before: Do full mock interviews with HireFlow AI simulating Amazon interview style.
• Day before: Review your notes, prepare questions for the interviewer, and get a good night of rest.

Practice Amazon Data Scientist Interview with HireFlow AI — our AI adapts to Amazon's interview style and gives real-time feedback.