The Ultimate guide to a Data Scientist Interview at Amazon

Do you realise while shopping on Amazon you end up buying more than what you intended to? This is mostly because of Amazon’s impressive algorithm that supplies you with suggestions of possible products that ‘you may like’. 

But how does this work? 

Amazon collects user data and uses that information to refine your searches, suggest products and therefore make further decisions.

Why Does Amazon Need Data Scientists to Run a Business?

Since Amazon’s business model relies a lot on data, the company needs a vast network of Data Scientists. It persistently looks for creative and motivated data scientists who will help them grow their data needs and business. 

At Amazon, the questions asked at the Data Scientist interview cover a wide range of topics which are mostly Amazon specific. Even though this sounds challenging, it should not stop anyone from trying their shot. 

Therefore without further ado let’s jump straight into the whole process of a Data Scientist interview at Amazon.

Amazon Data Scientist Interview Process

The whole process typically takes one to two months which begins with the application review and/or referral program (which can fasten the process). 

Pro tip: While curating your resume for Amazon, make sure to align it with Amazon’s Leadership Principles and do not forget to share a link of your LinkedIn profile because the recruiters at Amazon tend to look through such details before any form of interview.

After this the following stages of interviews take place:

Round 1- Telephonic Interview

This here is the more resume based interview where the recruiter will ask questions about you and your experience in general. The recruiter will also explain the job role in much more detail. 

The recruiter will also talk about where the team stands and how they contribute to the company. As a candidate, you need to show why you are a good fit at Amazon and what makes you stand out from the rest. 

Try and make the answer clear and direct so that any specifics can be discussed later.

Round 2-Technical Interview

This round of interview is all about how well a candidate fairs in the technical round of questions. It will consist of questions related to coding, statistics, tools, machine learning, etc. 

One should expect at least 2-3 coding like questions, which can either involve algorithm type or an SQL type. The coding portion is done via a shared code editor. For e.g.

• We ran an A/B test on two different sign up funnels. Write a query to see which variant ‘won’.
• What is OLAP and OLTP? And when do you denormalize data?

There may also be a round of questions regarding the approach you used and how you landed on the solution. You may also be subjected to machine learning type questions, therefore it is best to brush up all the knowledge and be prepared beforehand.

Round 3- On-Site Interview

Lastly, after all the successful rounds of interviews you may be called for an onsite, in person interview. Depending on the country, city you reside in and the Covid-19 situation, Amazon will schedule the interview at the nearest base of operations. 

This interview, like any other tech companies, will have 5 rounds of interviews with people from different roles at Amazon. They may be from business development, HR, data analytics and even department heads. 

Here you will be analysed for culture fit, leadership skills, critical thinking, behavioural aspects and another round of technical questions. 

Pro tip: For this interview, it is good to be well versed on technical aspects as Amazon interviewers love to ask such questions.

Amazon Data Scientist Interview – 20 Questions to Practice

Amazon Data Scientists must develop services and solve problems that are endlessly complex and constantly evolving. 

Depending on the role, your interviewer may ask you to define and discuss specific ideas around system design, questions on SQL, Statistics and machine learning models. Few questions that are commonly asked are:

Statistics and Probability questions

1. How does a neural network with one layer and one input and output compare to a logistic regression?
2. There are 4 red balls and 2 blue balls, what’s the probability of them not being the same in the 2 picks?
3. How would you explain hypothesis testing for a newbie?
4. What is the difference between linear regression and a t-test?

Machine Learning questions

1. How do you inspect missing data and when are they important?
2. How do you interpret logistic regression?
3. What are the feature selection methods used to select the right variables?
4. What is L1 vs L2 regularization?
5. What is cross-validation?
6. What is the difference between bagging and boosting?
7. What did you use to remove multicollinearity? Explain what values of VIF you used.
8. ‘People who bought this also bought…’ recommendations seen on Amazon are a result of which algorithm?
9. Explain different time series analysis models. What are some time series models other than Arima?
10. How does a neural network with one layer and one input and output compare to a logistic regression?
11.  How does dropout work?
12. Explain in detail how a 1D CNN works.
13. What is lstm? Why use lstm? How was lstm used in your experience?

SQL questions

1. Write a SQL code to explain month to month user retention rate.
2. Describe different JOINs in SQL.
3. What is the most advanced query you’ve ever written?
4. Given a table with three columns, (id, category, value) and each id has 3 or less categories (price, size, color); how can you find those id’s for which the value of two or more categories matches one another?

So there you have it. The ins and outs of a Data Scientist interview at Amazon.