Unlocking the Amazon Data Science Interview (18 Questions from 2024)
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With Amazon constantly growing and prioritizing customer satisfaction, scoring a Data Scientist position there is like hitting the jackpot in the data industry. I'll be upfront about my bias – having worked on Amazon's data projects and co-written a book with a seasoned FAANG Data Scientist friend, I can personally attest to the excitement and challenges you'll encounter.
But that doesn’t mean scoring a job at Amazon is impossible. I’m here to walk you through the interview process and leak questions that they recently asked during their interviews!!
What the Amazon Data Science Interview Process Looks Like
Landing a Data Scientist position at Amazon is a coveted opportunity, and the interview process is designed to identify the best candidates. Here's an overview of what to expect from start to finish:
1. Recruiter Screening
What to Expect: Be prepared to discuss your resume, your interest in Amazon, and your career goals. Highlight your soft skills and how they align with Amazon's leadership principles.
2. Technical Screening
What to Expect: The interviewer will evaluate your technical proficiency in SQL, Python, and machine learning. This stage may involve one or two rounds, depending on the role.
Insider Tip: Amazon values speed and accuracy in SQL. Many candidates are filtered out during this stage, so it's important to practice writing efficient SQL queries.
The best way to practice for the technical screen is to solve real SQL interview questions asked by TikTok. We covered these in our 10 TikTok SQL Interview Questions and built-in interactive coding pad for you to practice.
3. On-Site (Virtual) Interviews
What to Expect: This stage consists of five back-to-back virtual interviews, each lasting about 45 minutes. Each interview focuses on different aspects, including technical skills, data analysis and design, and behavioral questions.
Amazon Data Science Interview: Technical Questions
1. SQL Question: Write a SQL query to find the top 5 products with the highest sales in the last month.
2. SQL Question: Given a table of user activities, write a query to calculate the average session duration per user.
3. Python Question: Implement a function in Python to clean a dataset by removing duplicates and handling missing values.
4. Python Question: Write a Python script to merge two datasets on a common key and perform a group-by operation.
5. Machine Learning Question: Explain the difference between L1 and L2 regularization. When would you use one over the other?
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Amazon Data Science Interview: Data Analysis and Design Questions
6. Data Analysis: How would you design an experiment to test the effectiveness of a new recommendation algorithm on Amazon’s homepage?
7. Data Analysis: Given a dataset with user reviews, how would you identify and handle outliers?
8. Data Design: Describe how you would design a data pipeline to collect and process real-time user activity data.
9. Data Design: How would you approach building a dashboard to track the performance of a new product launch on Amazon?
Amazon Data Science Interview: Behavioral Questions
10. Leadership Principle: Customer Obsession: Tell me about a time when you went above and beyond to meet a customer’s needs.
11. Leadership Principle: Ownership: Describe a situation where you took complete ownership of a project and delivered outstanding results.
12. Leadership Principle: Dive Deep: Give an example of a time you had to dive deep into data to solve a complex problem.
13. Leadership Principle: Deliver Results: Can you share an experience where you were under significant pressure to meet a tight deadline? How did you ensure the project's success?
14. Leadership Principle: Bias for Action: Tell me about a time when you made a decision without having all the data you would have liked. What was the outcome?
15. Teamwork: Describe a time when you had to collaborate with a cross-functional team to complete a project. What challenges did you face, and how did you overcome them?
Amazon Data Science Interview: Scenario-Based Questions
16. Scenario: Imagine you are tasked with improving the accuracy of Amazon’s product recommendation system. How would you approach this project from start to finish?
17. Scenario: You notice a sudden drop in sales for a particular category on Amazon. How would you investigate and identify the cause?
18. Scenario: How would you handle a situation where your data analysis results contradict the prevailing business assumptions??
These questions cover a range of technical skills, data analysis and design, behavioral attributes, and scenario-based problem-solving, reflecting the comprehensive nature of Amazon's Data Scientist interview process.
However, even with these questions, there’s SO much more you can do to help you be prepared for your interview.
Resources to help you prepare for the Amazon Data Science Interview
DataLemur : Offers a collection of real interview questions and solutions specifically for data science roles at various companies, including Amazon. It also provides detailed explanations and insights into what interviewers seek in alternate solutions.
BONUS: Try out the blog 6 REAL Amazon SQL Questions for even more examples and solutions!
Amazon's Leadership Principles : Understanding Amazon's leadership principles is essential for behavioral interviews. Reviewing these principles and preparing examples of how you embody them will help you perform better in interviews.
“Cracking the Data Science Interview” by Maverick Lin : This book is a comprehensive guide specifically tailored for data science interviews. It covers a wide range of topics including coding, statistics, machine learning, and case studies, making it an excellent resource for preparing for technical questions.