Amazon Data Scientist Interview Guide

updated on 06 August 2022

Data roles in FAANG and other big tech companies are in great demand. Whereas, Data Scientist positions have become more popular now than ever. Candidates with an optimized resume and LinkedIn makeover, who have also taken mock interviews for interview prep have great chances of landing their dream FAANG Data Scientist job. Amazon hires a number of data scientists each year. It is a highly data-driven company, and working as an Amazon Data Scientist would be a dream come true for most job seekers! 🀩

This guide will provide comprehensive details about preparing for various Amazon Data Scientist interviews to help you land your dream offer! πŸ’―

Let's get started! πŸš€

Data Science Roles At Amazon πŸ“Š

Amazon is a technology company with multiple businesses and products like e-commerce (Amazon.com), cloud computing (AWS), digital streaming (Amazon Prime), artificial intelligence, and more. Being a company that leverages large amounts of data each day and delivers exceptional services through its products to customers, data scientists play a pivotal role at Amazon.

Amazon hires data scientists for various teams like AWS, Alexa, Amazon Go, Prime Video, Finance, and more.
Amazon hires data scientists for various teams like AWS, Alexa, Amazon Go, Prime Video, Finance, and more.

There are various kinds of roles at Amazon that could be a fit depending on the flavor of data science you are looking for (more research-focused, product focused, engineering focused, etc). They can be best categorized as follows:

1. Data Analyst/Business Intelligence/Business Analyst

These roles are more focused on the strategic side of the business. The responsibilities for these roles will mainly concentrate on dashboarding, forecasting, and transforming data to present it to leadership or towards business intelligence. 

Amazon has vast analyst and business intelligence teams, and data scientists often work closely or collaborate with such teams.

2. Machine Learning Engineer

Amazon Machine Learning Engineers are candidates who generally have a software engineering background. Although this position isn’t exactly a data science role, MLEs at Amazon are responsible for rolling out production models using their knowledge of software engineering, machine learning, and statistics while working closely with the company's data scientists and business stakeholders.

Some teams within Amazon that hire a lot of machine learning engineers are Alexa, AWS, and Amazon.com. Working as a Machine Learning Engineer at Amazon can be a great step for software engineers who want to kick-start their data science journey or work in a field that is closer to the data science track.

3. Data Scientist 

The responsibilities of an Amazon Data Scientist vary depending on their team, product, and project. However, Data Scientists' major responsibilities are creating models, delivering analytical solutions, and running A/B testing, besides collaborating with other teams to enhance client success or customer experience.

Data Scientists at Amazon use data to generate insights and develop models to achieve specific outcomes.

4. Applied Scientist/Machine Learning Scientist

Often confused with the role of a machine learning engineer, applied scientists/machine learning scientists focus on the application of ML models built by data scientists and the scalable pipelines and systems developed by MLEs to support these ML models. 

Like machine learning engineers, applied scientists at Amazon are expected to have strong fundamentals in statistics, machine learning, SDE coding and ML algorithms.

5. Research Scientist

The last type of data science role at Amazon is Research Scientist. They are tasked with owning the delivery of modeling solutions for an entire business application and applying advanced scientific methods and principles, mathematical theory, and/or statistical analysis to improve existing approaches.

This role is often called AI Researcher at other FAANG and top tech companies, where candidates are expected to research the state-of-the-art ML and AI techniques that have broad usage across products.

Interview Process and Timeline⏳

The interview process and timeline for the Amazon Data Scientist position are similar to that for other roles.

1. Recruiter/HR Call

A 20-30 minutes straightforward call with a recruiter/HR from Amazon to learn more about you, your background, some questions from your past experience, and your interest in the position. The recruiter will also discuss the logistics and brief you about what the interview process will look like going forward.

A few tips for doing well in this call are:

  • Prepare a short and impressive elevator pitch. Talk about something that isn't already there in your resume.
  • Stay relaxed and leave a positive impression through the call.
  • Have a clear answer ready for "Why you want to work at Amazon" and "What about the role you have applied for excites you."
  • Be ready with any interview process-related questions.

2. Technical Screen (1-2 Phone Screens)

Like the technical screens for other FAANGs, Amazon's Data Scientist role technical screening will last 45-60 minutes. It will assess your behavioral fit and technical competency by focusing on concepts of statistics, machine learning, SQL, and coding problems. These may vary slightly from one data role to another, between teams, and from one candidate to another too. For the technical screen, you can expect two coding questions, one SQL question, one machine learning concept question, and an algorithm coding type question.

To do well in the technical screen round (some candidates may have more than one technical screen before an onsite), practice the following concepts:

  • Python Algorithm Coding
  • ML Questions/Case Problems
  • Statistics Questions
  • SQL Questions
  • Behavioral Questions (Amazon Leadership Principles)

Here are two examples of SQL technical screen questions:

1. Given a users table, write a query to get the cumulative number of new users added by the day, with the total reset every month.

2. We have two tables. Table A has one million records with fields ID and AGE. Table B has 100 records with two fields, ID and SALARY. In Table B, the mean salary is 50k, and the median salary is 100k. 

SELECT A.ID, A.AGE, B.SALARY
FROM A
LEFT JOIN B
ON A.ID = B.ID
WHERE B.SALARY > 50000

Given the query above is run, how many records would be returned?

Pro Tip: While solving the coding and SQL questions, don't forget to share your approach and thought process. Keep the interview conversational, and share your thoughts as you solve the questions.

Also Read: Amazon Software Engineer Interview Guide

3. Onsite Interviews (5-6 Rounds)

While interviewing for this role, you may undergo 5-6 onsite/virtual interviews. They will be 45-60 minutes each and test you on several aspects of the position. These can be challenging to crack, especially if you've not prepared well.

Onsite interviews at Amazon are generally back-to-back, with a 15-minute gap between one interview and another.
Onsite interviews at Amazon are generally back-to-back, with a 15-minute gap between one interview and another.

The onsite interviews will mainly focus on the following areas:

  • Algorithms and Optimizations
  • Machine Learning and Modeling Case Questions
  • Technical Interview involving statistics concepts, data analysis, and A/B testing
  • SQL interview with a Data Scientist
  • Behavioral Interview to assess culture fit

Note: You may or may not have a separate round for the behavioral interview. Regardless, Amazon assesses all candidates on their 16 leadership principles in each round. Even in technical interviews, make sure your answers convey that you have an understanding of the 16 LPs and can implement them in the decisions you make.

Amazon Data Science Example Interview Questions πŸ“

Machine Learning Questions

  1. How do you perform feature selection?
  2. What is the difference between bagging and boosting?
  3. Suppose you increase the minimum sample size per leaf in terminal nodes. In a random forest model, what happens to the variance and bias?
  4. What did you use to remove multicollinearity? Explain the VIF values you have used.
  5. How would you design a recommender system for the Amazon grocery app?
  6. How would you build a forecasting model?
  7. What is L1 vs. L2 regularization?
  8. What is the variance-bias trade-off?
  9. Describe a case where you have solved an ambiguous business problem using machine learning.
  10. You are tasked with building a model to detect potential bombs at a border crossing. How would you design the model's inputs and outputs, measure its accuracy, and test your model?

Statistics Questions

  1. What is the logistic regression model?
  2. What is the p-value?
  3. How does the presence of an outlier affect the assumption of a linear model?
  4. How would you explain hypothesis testing for a newbie?
  5. How do you interpret OLS regression results?
  6. How would you design an A/B test for a marketing campaign?
  7. Can you explain type I, II, and III error rates?
  8. How are linear regression and a t-test different?
  9. The probability that an item is at location A is 0.6, and 0.8 at location B. What is the probability that an item would be found on the Amazon website?
  10. Name the five assumptions of linear regression.

Coding Questions

Coding questions for the data science interview can be divided into three categories - algorithms and data structures, statistical coding, and table manipulation in SQL.

Algorithms and Data Structures

  • Given an unfair coin with the probability of heads not equal to .5, what algorithm could you use to create a list of random 1s and 0s?
  • Write a python code to recognize whether entries to a list have the same characters. Then what is its computational complexity?
  • Given a large string and a smaller string, write a program to find out if the smaller string can be generated from letters from the larger string.

Statistical Coding

  • Given the list of actual and predicted values, compute the MAPE.
  • Assume you have a file containing data in the form of data = [{"one":a1, "two":b1,...},{"one":a2, "two":b2,...},{"one":a3, "two":b3,...},...] How could you split this data into 30% test and 70% train data?
  • How does K-means work? What kind of distance metric would you choose?

Table Manipulation using SQL

  • Given a table with three columns (id, category, value) and each id has three or fewer categories (price, size, color); how can you find those IDs for which the value of two or more categories matches one another? 
  • Implement the union and intersection of two arrays (in an efficient way). Note that elements of the two given arrays may be repeated, but cannot be repeated in union and intersection arrays.
  • Given a CSV file with ID and Quantity columns, 50 million records, and the size of the data is 2gig, write a program to aggregate the QUANTITY column.

Behavioral Questions

  1. Tell me about a time you solved a problem for a customer and how did you overcome any challenges in the process? 
  2. Tell me about a time you did something at work that wasn't your responsibility.
  3. Tell me about a time when you launched a feature with known risks.
  4. Tell me about a time you had a conflict with a manager and how you approached it.
  5. What is the most innovative idea you've ever had?
  6. Tell me about the most complex problem you have worked on.
  7. Tell me how you deal with ambiguity.
  8. How do you prioritize tasks in your current role?
  9. Tell me about a time you stepped in to help a struggling teammate.
  10. Tell me about the last time you figured out a way to keep an approach simple or save on expenses.

You can learn more about the Amazon Leadership Principles through here: Amazon Behavioral Interview Questions Guide

Tips To Ace The Interview πŸŽ―

Acing a FAANG interview is often about attention to the little details. Here are a few tips our FAANGPath mentors would give candidates during mock interviews that might be helpful for you. πŸ‘‡πŸ» 

  1. Focus on technical competency. For data roles, Amazon lays great emphasis on the technical abilities of a candidate. Don't forget to brush up on your statistics and machine learning concepts, algorithms and data structures, and knowledge about building ML models. Also, specifically for Data Scientist interview rounds, try to cite real-life examples from your past projects and experiences when you are clarifying any technical concept. 
  2. Share your thought process with the interviewers. Don't simply solve a question and share the answer with them. Instead, share your approach for the answer, why you are doing what you're doing, and help them understand your strategy.
  3. DO NOT OVERLOOK THE AMAZON LEADERSHIP PRINCIPLES. Unlike other FAANG companies, Amazon takes its LPs very seriously. Candidates are assessed on these at each step of the interview process, so each time you see a chance to demonstrate examples tied to these principles, grab it. Candidates are recommended to answer the LP questions in STAR (Situation, Task, Action, Result) format and also follow the XYZ approach (Accomplished [X] as measured by [Y], by doing [Z]) in their answers.
  4. Practice for case questions. Amazon often asks candidates to apply modeling and machine learning concepts in a business scenario to solve a problem. The best approach to such questions is to clarify, state assumptions, hypothesize and propose a solution, provide data points and analysis, consider potential pitfalls, and conclude the case. Additionally, try to add instances of numerical quantification in your answers, both in the form of back-of-the-envelope calculations and approximate ballpark figures. 
  5. Communicate effectively. Regardless of the role you apply for, communication is always crucial to succeeding in your workplace. 

Got An Offer From Amazon - What Next? πŸ€”

If you've been offered a position by Amazon, first of all, CONGRATULATIONS! πŸŽ‰ You made it so far, and we're proud of you! ❀️

But this is not it. You can still do a few things to ensure you understand your offer well and negotiate for the pay you deserve.

Here are a few things you can do next:

  • Resist the urge to sign the offer and send it back immediately. Take your time to read the offer thoroughly. Make sure you understand everything and seek professional advice if necessary.
  • Check if the compensation matches the market range for your role with the years of experience you have and the skills you bring to the table. 
  • Negotiate the offer if required. Acknowledge the offer, reinforce your achievements, state the gap and share your counteroffer. You can read more about it on this LinkedIn post by our founder, Nikita Gupta - Salary Negotiation Advice
  • If you think the salary is non-negotiable, keep in mind that you can also negotiate the equity, paid time off, benefits, more WFH days, and other elements of your offer.

Also Read: Understanding Salary Bands for FAANG+ Companies

Conclusion βœ…

Landing a FAANG offer out of 100s of applications and dozens of interviewees is not an easy task. But proper preparation can help you achieve your goal and succeed in your job search. Contact FAANGPath now and schedule a mock interview with our FAANG mentors. 

Let us help you land your dream job! 🀝

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