Top 25 Amazon Data Scientist Interview Questions + Guide in 2025

Breaking into FAANG companies like Amazon is no easy feat, especially for data science roles. Amazon deals with massive datasets and complex real-world problems, powering everything from product recommendations to logistics optimization. As a data scientist working in a big tech company, you’ll work on cutting-edge machine learning (ML) models and data-driven solutions.

Given the scale of Amazon’s data science operations, data science roles are consistently opening up within the company. The hiring process is rigorous, involving several stages such as recruiter screening, technical screening, and on-site interviews. However, landing a data scientist role at Amazon is a highly sought-after achievement. The company provides a fast-paced, innovative environment where data scientists can drive real impact, collaborate with top professionals, and work on large-scale challenges that shape the future of e-commerce, logistics, and AI.

To increase your chances of landing the job, prepare thoroughly. This includes reviewing key concepts, practicing problem-solving, and rehearsing your talking points for potential interview questions. Let this guide walk you through the Amazon interview process, including practice questions, links to helpful resources, and preparation tips to help you land the Amazon data scientist role.

Amazon data scientists connect business, customers, and technology by analyzing massive datasets across various domains like retail, AWS, logistics, and Alexa. They develop data-driven solutions that optimize operations, enhance customer experiences, and drive innovation.

Depending on the team, their work can include:

• Digital & Alexa Support (D2AS): Using data-driven insights to improve digital support experiences, optimizing chatbot responses, enhancing Alexa’s voice recognition, and improving self-service support tools.
• Amazon Logistics (AMZL) & Last Mile: Optimizing last-mile delivery with advanced analytics, machine learning, and optimization modeling, focusing on route efficiency, predicting delivery delays, and optimizing driver assignments.
• Retail & E-commerce: Enhancing product recommendations, pricing strategies, and demand forecasting to improve customer shopping experiences. This involves personalization, customer segmentation, fraud detection, search optimization, and inventory management.
• Amazon Prime: Analyzing user behavior to improve subscription retention and optimize delivery speed, such as optimizing Prime Video recommendations, personalizing Prime Day deals, and developing new Prime benefits.
• Amazon Fresh: Develop machine learning models to optimize grocery operations, ensuring better inventory management and product availability. This includes building algorithms for vendor selection and product assortment, as well as developing pricing anomaly detection systems to flag incorrect prices and identify mismatches in product attributes.

Amazon data scientists are hired at different levels, ranging from L4 (entry-level) to L6+ (senior roles), with salaries varying based on experience and location. Compensation can range from $181,000–$264,000 per year, including base salary, bonuses, and stock options.

Qualifications

Amazon sets high standards for hiring top data professionals. The general qualifications for a data scientist role include:

Must Have:

• Master’s degree or above in a quantitative field such as statistics, mathematics, data science, or computer science.
• 2+ years of experience in machine learning, statistical modeling, data mining, and analytics.
• 2+ years of experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical/mathematical software (e.g., R, SAS, Matlab).
• Proven ability to communicate technical concepts effectively at a level appropriate for the audience.

Nice to Have:

• Experience as a machine learning or data scientist in a large technology company.
• Hands-on experience applying theoretical models to real-world problems.
• Proficiency with machine learning/statistical modeling tools and understanding of key parameters that impact performance.

Recruiters follow a thorough process to identify the best candidates for a certain role, and Amazon is no exception. Their interview process typically spans two to three weeks. Here’s an overview of what to expect:

1. Recruiter Screening (30–45 minutes)

   The hiring process begins once you’ve submitted your application and been contacted by a recruiter. The first step is an initial screening call, which primarily covers behavioral questions. This conversation focuses on past challenges you’ve encountered and how you handled them, using Amazon’s Leadership Principles to guide the discussion. This stage is usually conducted by a recruiter or talent acquisition specialist.

2. Technical Screening (1 or 2 screens, 45–60 minutes each)

   During the technical screening, the interviewer will evaluate your proficiency in areas such as statistics, SQL, Python, and machine learning. Using CollabEdit, you’ll solve coding challenges while the interviewer observes and interacts. You may also be asked to explain your approach. This stage will not only assess your technical proficiency but also evaluate your critical thinking, problem-solving skills, and communication skills.

3. On-site Interview (5–6 rounds, 60 minutes each)

   If you pass the technical screen, the next step is an on-site interview at Amazon’s offices. The interviews focus on technical skills, machine learning, and behavioral questions. You’ll meet with multiple interviewers, such as the hiring manager, senior data scientists, and other team members, who will evaluate your suitability for the role from different perspectives.

The Amazon data science Interview questions can be broken into five areas:

• Machine Learning
• Python
• SQL
• Statistics
• Behavioral Questions

In addition to these topics, make sure to review data structures and algorithms (DSA). While often used with Python, DSA focuses on topics like arrays, hash maps, trees, and graph algorithms, which are commonly tested in coding tasks.

For this role, both machine learning and DSA are critical. At Amazon, DSA helps manage massive datasets and optimize real-time systems like inventory tracking, recommendation engines, and fraud detection. Meanwhile, machine learning powers predictive models for demand forecasting, personalized recommendations, and logistics optimization.

Amazon Machine Learning Questions

Machine learning questions in interviews typically focus on fundamental concepts, practical applications, and computing/coding.

1. What are the assumptions behind logistic and linear regression, and how do they impact the model’s performance and applicability?

Linear and logistic regression rely on specific assumptions about data distribution, relationships between variables, and independence. Understanding these assumptions helps ensure the models perform accurately and are applied correctly in different scenarios.

2. How does the k-means clustering algorithm work, and what distance metric would you use?

k-means is an unsupervised learning algorithm that groups data points into clusters based on similarity. The choice of distance metric, such as Euclidean or Manhattan distance, affects how clusters are formed.

3. If you could build a perfect classification model with 100% accuracy to predict customer behavior, what potential issues could arise when applying this model in real-world scenarios?

A model with 100% accuracy might seem ideal, but in real-world applications, it could indicate overfitting or an inability to generalize to unseen data.

4. How would you model user behavior on the Amazon website to predict if their next action will result in a purchase?

Predicting user behavior on a website involves analyzing user interactions, past behavior, and engagement patterns. Models like sequence-based approaches or machine learning techniques help estimate the likelihood of a purchase.

5. If the probability of a user clicking on a product recommendation is higher on the homepage than in search results, how would you calculate the overall probability of the user clicking on the product during their session on Amazon?

Click probabilities across different website sections need to be combined mathematically to determine the likelihood of a user clicking on a product during a session. This involves probability rules and understanding user navigation behavior.

It’s important that you have a strong understanding of machine learning algorithms and their real-world applications, whether it’s modeling user behavior, addressing model limitations, or calculating probabilities.

Amazon Python Questions

Evaluating your Python skills is an important part of the Amazon data scientist interview process. This involves testing your knowledge of Python libraries (like pandas), data manipulation, algorithm efficiency, and coding ability.

6. How would you drop rows with missing values in a pandas DataFrame?

This question focuses on data cleaning, specifically on handling missing values in a dataset. You should be familiar with the methods to identify and remove rows containing NaN (missing) values. You can use a function in the pandas library to handle this issue.

7. How would you merge two sorted lists into one sorted list in Python? Can you explain the process and its efficiency?

You can use a two-pointer technique by iterating through both lists, comparing the current elements, and adding the smaller one to a new list. It’s important to consider the time complexity of the process, especially with large lists, as it typically runs in O(n) time.

8. Create a Python function that takes two lists and returns a list of their common elements. For example, if given [1, 2, 3] and [2, 3, 4], the function should return [2, 3].

To identify elements that appear in both lists, you can either convert the lists to sets and use the intersection operation or iterate through one list and check for membership in the second list.

9. Write a Python function that checks for duplicates in a list. What strategies would you use to improve its performance for large lists?

For this question, you can consider different approaches, such as using loops or set intersections, to find common elements efficiently.

10. Write a Python script to clean a dataset by removing rows with missing values and filling the missing entries with the median value of the corresponding column.

Cleaning data involves handling missing values. Using dropna() can remove rows with missing values, while fillna() can fill the missing data with a specified value, like the median of the column. The median is used because it is less sensitive to outliers compared to the mean.

Read on more Python questions and review topics you might have missed out on.

Amazon SQL Questions

Amazon generates large datasets daily, and your SQL proficiency will be a critical focus during the interview. You’ll be assessed on your ability to write SQL queries, manipulate large datasets, and extract actionable insights to support data-driven business decisions.

11. Explain the different types of JOINs in SQL. Provide examples of when they are used.

SQL JOINs are used to combine rows from two or more tables based on a related column. You can explain its different types by providing what each does and when they can be used.

12. Given a table of product purchases, write a query to find the number of users who made additional purchases after their first one.

The goal here is to calculate the percentage of transactions delivered to a user’s primary (home) address. The query counts transactions to the home address and all transactions for each user, then calculates the percentage.

13. Given a table of product purchases, write a query to find the number of users who made additional purchases after their first one.

Your query should identify users who have purchases beyond their first, ensuring that purchases on the same day aren’t counted as upsells.

14. Write a query to calculate the average delivery time for orders in the orders table. Assume columns order_date and delivery_date are available.

To get the maximum quantity purchased for each distinct product_id every year, you’ll need to group the data by year and product_id, then find the maximum quantity within each group.

15. Given the transactions and products tables, write a query to find the top five most frequently purchased product pairs by the same user.

For this, you can use a self-join on the transactions table to pair products purchased by the same user in the same transaction. By ensuring that each product pair is counted only once and grouping by product pairs, you can count how often each pair is purchased together.

Practice more SQL questions to master writing complex SQL queries.

Amazon Statistics Questions

Statistics is an integral part of the Amazon data scientist interview, as it is important for analyzing data, building models, and making data-driven decisions. To succeed in this role, you should be familiar with statistical concepts and techniques.

16. How would you explain the R² value?

R² measures how well the independent variables explain the variation in the dependent variable. A value of 1 means perfect explanation, while 0 means no explanation. It shows the model’s accuracy in predicting outcomes.

17. Can you explain the concept of statistical significance? How would you determine if a new marketing campaign has had a significant impact on sales?

Statistical significance tests if a result is likely due to a real effect, not chance. You can determine the impact of a marketing campaign by comparing sales before and after using hypothesis testing. A low p-value indicates a significant effect.

18. Describe how you would use regression analysis to predict future sales based on historical data. What factors would you consider including in your model?

Regression analysis finds relationships between sales (dependent variable) and predictors (independent variables). Factors like seasonality, marketing, pricing, and competition would help predict future sales. When answering, you should explain that the goal of regression is to build a predictive model using these factors and estimate future sales based on them.

19. Explain how you would measure customer satisfaction using survey data. What statistical techniques would you apply to analyze the results?

Customer satisfaction can be measured using surveys with rating scales (e.g., Likert scale) or open-ended questions. You can then analyze correlations between different satisfaction aspects.

20. What is the difference between correlation and causation? How would you demonstrate that a new feature on the Amazon website has caused an increase in sales?

The argument here is that correlation doesn’t equal causation. Just because Amazon smart speakers and Prime subscriptions both increase doesn’t mean one causes the other.

Explore more: Top 49 Statistics Interview Questions.

Amazon Behavioral Questions

Amazon data science interviews focus not only on your technical skills but also on your behavioral qualities. Be prepared to answer questions about your past experiences or challenges you’ve faced. In answering each question, you can align your answer with Amazon Leadership Principles to show your genuine interest in the role and that your values align with theirs.

21. Tell me about a time you faced a significant challenge in a project. How did you approach it, and what was the outcome?

This question assesses “Deliver Results” and “Bias for Action.” Describe a challenging project you’ve worked on, explaining how you proactively addressed the obstacles that came up. Focus on how you remained results-oriented and took decisive actions to overcome the challenges.

22. Describe an instance where you used data to improve a process or product. What was the impact?

This relates to the “Customer Obsession” principle. Explain how you used data to drive improvements that benefitted the customer or simplified a process. Discuss the measurable impact it had on performance, efficiency, or customer satisfaction.

23. Explain how you have contributed to a team project. What role did you play, and what was the result?

One of Amazon’s principles is “Teamwork.” For this, you can share your contributions to the team, emphasizing how you supported others, collaborated effectively, and helped deliver collective results.

24. Why do you want to work at Amazon, and how do our leadership principles resonate with your personal values?

Show how Amazon’s principles, such as a strong focus on customers and driving long-term impact, reflect your own values and how they motivate your desire to work there.

25. Tell me about a time when you had to work under a tight deadline. How did you prioritize your tasks?

Align your answer with the “Invent and Simplify” principle. Explain how you managed competing priorities, stayed focused on important tasks, and delivered results under pressure. Highlight how you maintained efficiency and quality even with tight deadlines.

Amazon, as one of the big tech companies, selects only the most qualified candidates for its data scientist roles. So when you’re given the chance to do the interview process, make sure that you’re well-prepared and keep your knowledge sharp.

Follow these tips to ace your interview:

• Focus on improving your problem-solving abilities in the context of large-scale data.
• Work on coding challenges, data manipulation tasks, and case studies that reflect Amazon’s business model to enhance your analytical and technical skills.
• Be ready to discuss how you’ve applied data science techniques to solve complex problems and deliver business value.
• Use the STAR method (Situation, Task, Action, Result) to clearly communicate your past experiences and problem-solving approaches.
• Don’t forget to familiarize yourself with their 16 leadership principles, as many behavioral questions will be framed around them.

1. Interview Query: Check our website and practice data science interview questions. Access our learning paths and the resources we offer.

2. Explore more Amazon data scientist interview questions here. You can filter by topics like statistics, SQL, machine learning, and more to focus on the areas you want to practice.

3. Prepare for case studies: Data Science Case Study Interview Questions.

4. Boost your confidence by participating in real-time mock interviews with like-minded peers.

5. Watch the Amazon Data Science Interview discussion.

These resources will help you prepare for your Amazon data scientist interview and maximize your chances of success. Stand out and land a data scientist role at Amazon!