Amazon Data Scientist Interview Guide (27 Questions Asked in 2024)
With Amazon's ever-expanding reach and its relentless pursuit of customer satisfaction, landing a role as a Data Scientist here is like hitting the jackpot in the data world. And let me confess my bias upfront – having been part of Amazon's data-driven journey and co-authoring a book with my buddy, a seasoned FAANG Data Scientist, I can attest to the thrill and challenges that await you.
In this Amazon Data Science Interview guide, we’ll cover:
- Stages of the interview process
- Machine Learning Questions
- Python Questions
- SQL Questions
- Statistics Questions
- Behavioral Questions
- 6 Resources to Prepare for the Amazon Data Science Interview
The Amazon Data Scientist Interview Process
The interview process for Amazon from beginning to end is about 1 month long. During that time you will have multiple rounds of interviews with several senior members of the Data Science team. Here are those sections:
Round 1: Recruiter Screening
The first step in the interview process, but don’t underestimate it. Use this as an opportunity to highlight your soft skills, that might not be easy to find on your resume.
- 💼 Format: Phone Call
- ⏰ Duration: 30-45 minutes
- 👤 Interviewer: Recruiter or Talent Acquisition Specialist
- ❓ Questions: Culture fit, Understanding your Experience, Logistics
Round 2: Technical Screening
The technical screen is typically conducted on a service link called “CollabEdit” where the interviewer can watch and assess your work. For this role, you can have between 1~2 technical screens.
- 💼 Format: Virtual video call
- ⏰ Duration: 45 - 60 minutes
- 👤 Interviewer: Hiring Manager/Senior Data Scientist
- ❓ Questions: Technical Skills (SQL+Python), Machine Learning
Insider Tip: Amazon needs you to be very fast & accurate with writing SQL. They have thousands of people who apply for this role, and the SQL screen is an easy black-and-white filter to remove candidates, so you should aim for flawless execution here.
The best way to practice for the technical screen is to solve real SQL interview questions asked by Meta. We covered these in our article 6 REAL Amazon SQL Interview Questions and built an interactive coding-pad to help you practice:
Final Round: On-site Round
Anywhere from 1 to 3 weeks following the Technical Screen, you will hear if you’ve moved to the next round. The On-Site interview is split into 5 back-to-back interviews of 45 minutes each focusing on a different topic.
- 💼 Format: Virtual video call
- ⏰ Duration: 45 minutes each
- 👤 Interviewer: Hiring Manager/Senior Data Scientist
- ❓ Questions: Data Analysis and Design, Technical Analysis, Behavioral questions
Interview Questions
The Amazon Data Science Interview questions can be broken into 5 types:
- 🤖 Machine Learning
- 🐍 Python
- 💾 SQL
- 📊 Statistics
- 🧠 Behavioral Questions Use these sample questions as a baseline for your current preparedness and understand where you excel and what areas you need to improve on.
Amazon Machine Learning Questions
These questions are tailored to Amazon's business model and areas where machine learning can play a significant role in improving various aspects of its operations and customer experience.
- How would you optimize Amazon's recommendation system to improve customer engagement and increase sales?
- Explain how you would leverage machine learning to enhance Amazon's fraud detection capabilities and ensure a secure online shopping experience for customers.
- Amazon handles vast amounts of data. Can you discuss a scalable approach to analyzing this data and extracting valuable insights using machine learning techniques?
- Describe how you would use machine learning to optimize Amazon's supply chain management, ensuring timely delivery and minimizing costs.
- Amazon is known for its customer-centric approach. How would you utilize machine learning to personalize the shopping experience for each customer and increase customer satisfaction and loyalty?
Want more questions? Try these 70 Machine Learning Interview Questions & Answers.
Amazon Python Questions
Amazon's data science interviews often cover Python proficiency, focusing on practical applications and problem-solving skills. Expect questions ranging from data manipulation and analysis using libraries like Pandas to scalable solutions leveraging AWS services and efficient coding practices in Python.
- How would you use Python to efficiently parse and analyze large log files generated by Amazon Web Services (AWS) services?
- Can you explain the difference between list comprehension and generator expressions in Python? When would you use each one, and why?
- Amazon operates on a massive scale. How would you design a Python script to automate the process of deploying and managing resources on AWS using the Boto3 library?
- In a distributed system like Amazon's, how would you approach handling concurrency and parallelism in Python to optimize performance?
- Write a function to get the intersection of two lists: For example, if A = [1, 2, 3, 4, 5], and B = [0, 1, 3, 7] then you should return [1, 3].
Looking for more Python Interview Questions? Check out DataLemur!
Amazon SQL Questions
In Amazon's data science interviews, SQL questions typically revolve around querying and analyzing large datasets to derive insights relevant to the business. Expect questions that assess your ability to write complex SQL queries, optimize query performance, and manipulate data efficiently to solve real-world problems encountered at Amazon.
1. Average Review Ratings
Given the reviews table, write a query to retrieve the average star rating for each product, grouped by month. The output should display the month as a numerical value, product ID, and average star rating rounded to two decimal places. Sort the output first by month and then by product ID.
Table:
| Column Name | Type |
|---|---|
| review_id | integer |
| user_id | integer |
| submit_date | datetime |
| product_id | integer |
| stars | integer (1-5) |
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 06/08/2022 00:00:00 | 50001 | 4 |
| 7802 | 265 | 06/10/2022 00:00:00 | 69852 | 4 |
| 5293 | 362 | 06/18/2022 00:00:00 | 50001 | 3 |
| 6352 | 192 | 07/26/2022 00:00:00 | 69852 | 3 |
| 4517 | 981 | 07/05/2022 00:00:00 | 69852 | 2 |
Example Output:
| mth | product | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Explanation: Product 50001 received two ratings of 4 and 3 in the month of June (6th month), resulting in an average star rating of 3.5.
The dataset you are querying against may have different input & output - this is just an example!
2. Highest-Grossing Items
This is the same question as problem #12 in the SQL Chapter of Ace the Data Science Interview!
Assume you're given a table containing data on Amazon customers and their spending on products in different category, write a query to identify the top two highest-grossing products within each category in the year 2022. The output should include the category, product, and total spend.
Table:
| Column Name | Type |
|---|---|
| category | string |
| product | string |
| user_id | integer |
| spend | decimal |
| transaction_date | timestamp |
Example Input:
| category | product | user_id | spend | transaction_date |
|---|---|---|---|---|
| appliance | refrigerator | 165 | 246.00 | 12/26/2021 12:00:00 |
| appliance | refrigerator | 123 | 299.99 | 03/02/2022 12:00:00 |
| appliance | washing machine | 123 | 219.80 | 03/02/2022 12:00:00 |
| electronics | vacuum | 178 | 152.00 | 04/05/2022 12:00:00 |
| electronics | wireless headset | 156 | 249.90 | 07/08/2022 12:00:00 |
| electronics | vacuum | 145 | 189.00 | 07/15/2022 12:00:00 |
Example Output:
| category | product | total_spend |
|---|---|---|
| appliance | refrigerator | 299.99 |
| appliance | washing machine | 219.80 |
| electronics | vacuum | 341.00 |
| electronics | wireless headset | 249.90 |
Explanation: Within the "appliance" category, the top two highest-grossing products are "refrigerator" and "washing machine."
In the "electronics" category, the top two highest-grossing products are "vacuum" and "wireless headset."
The dataset you are querying against may have different input & output - this is just an example!
Highest-Grossing Items
3. Cumulative Purchases by Product Type
This is the same question as problem #4 in the SQL Chapter of Ace the Data Science Interview!
Assume you're given a table containing Amazon purchasing activity. Write a query to calculate the cumulative purchases for each product type, ordered chronologically.
The output should consist of the order date, product, and the cumulative sum of quantities purchased.
Table:
| Column Name | Type |
|---|---|
| order_id | integer |
| product_type | string |
| quantity | integer |
| order_date | datetime |
Example Input:
| order_id | product_type | quantity | order_date |
|---|---|---|---|
| 213824 | printer | 20 | 06/27/2022 12:00:00 |
| 132842 | printer | 18 | 06/28/2022 12:00:00 |
Example Output:
| order_date | product_type | cum_purchased |
|---|---|---|
| 06/27/2022 12:00:00 | printer | 20 |
| 06/28/2022 12:00:00 | printer | 38 |
Explanation: On June 27, 2022, a total of 20 printers were purchased. Following that, on June 28, 2022, an additional 38 printers were purchased, resulting in a cumulative total of 58 printers (20 + 38).
The dataset you are querying against may have different input & output - this is just an example!
Try these 6 Amazon SQL Interview Questions!
Amazon Statistics Questions
Amazon's data science interviews often include statistical questions that focus on practical applications, such as designing experiments, analyzing large datasets, and making data-driven decisions. Expect questions that require you to demonstrate proficiency in hypothesis testing, regression analysis, and experimental design, tailored to solving real-world problems encountered at Amazon.
- How would you design and interpret an A/B test to evaluate the effectiveness of a new feature on Amazon's website?
- Amazon deals with large datasets. Can you discuss a statistical approach to identify trends and patterns in customer behavior data to improve marketing strategies?
- Describe the process of designing a statistical model to predict product demand for Amazon's inventory management system. What factors would you consider, and how would you validate the model?
- Amazon aims to optimize its delivery network. How would you use statistical methods to analyze delivery times and identify areas for improvement?
- Amazon values customer satisfaction. Can you propose a statistical approach to analyze customer feedback data and prioritize areas for product and service enhancements?
Try these 20 Statitics Questions asked in the Data Science Interview!
Amazon Behavioral Questions
Amazon's data science interviews often include behavioral questions that focus on past experiences and how they align with Amazon's leadership principles. Expect questions that explore your problem-solving approaches, collaboration skills, innovation, adaptability, and response to feedback.
- Tell me about a time when you had to make a decision based on incomplete or ambiguous data. How did you approach the situation, and what was the outcome?
- Can you describe a challenging project you worked on where you had to collaborate with cross-functional teams? What were the key challenges, and how did you overcome them?
- Amazon values innovation. Can you share an example of a creative solution you implemented to solve a problem in a previous data science role?
- Describe a situation where you had to prioritize multiple competing tasks or projects. How did you manage your time effectively, and what was the result?
- At Amazon, we strive for continuous improvement. Can you discuss a time when you received constructive feedback on your work? How did you handle it, and what did you learn from the experience?
You should also study the Amazon leadership principles to pass the tricky bar-raiser rounds. For a deep dive into the Amazon 16 leadership principles, along with potential behavioral questions you'll get at Amazon check out our Amazon Behavioral Interview Question Guide.
Preparation Tips for the Amazon Data Scientist Interview
Now that you’ve learned everything there is to know about the interview process it’s time to prepare. You must navigate the interview process with confidence and precision, so take the time to prepare and refresh both your hard and soft skills.
Tips for the Day of Your Amazon Interview:
- Think out loud 🤔: Provide a narrative as you go through the problem so that the interviewer has insight into your thought process.
- Deconstruct your problems 🛠️: Deconstruct complicated or ambiguous problems into groups, and combine the groups for a solution.
- Hints 💡: Pivot your answer if your interviewer prompts you that you’re heading in the wrong direction.
- Clarification 🔍: Ask clarifying questions during the interview.
- Say why you’re interested in a career at Amazon 🌟: Amazon interviewers like to see people who know about our environment, projects, challenges, etc.
- Questions ❓: Ask questions about Amazon and analytics if there’s time.
Best Resources to Prepare for the Amazon Data Science Interview
- DataLemur: Python and SQL preparation with questions from the Amazon Interview
- Khan Academy Statistics and Probability Course: good for the Amazon analytical execution questions
- Cracking the PM Interview by Gayle Laakman McDowell: good for the Amazon Case study questions
- Ace the Data Science Interview: written by 2 Ex-Facebook employees, this is the go-to resource for Acing the Amazon Data Science Interview. The book has 201 real FAANG interview questions, including 11 from Facebook.
- A/B testing Questions Blog: this guide walks you through how to run consumer experiments, which is a frequent topic due to how important product experimentation & interpreting test results is for Amazon Data Science roles
- Amazon Careers Website: Visit this site to learn more about the company culture, values, and available data science roles