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Amazon Data Scientist Interview Guide

A complete guide on the Amazon data scientist interview process, tips and tricks to ace the interview, and most importantly, the kind of questions asked in the interviews at Amazon.


One of the most popular and household names in recent times has been Amazon. It started off as a simple marketplace to sell books and has now grown to become a synonym for online shopping, covering various industries such as electronics, software, video games, apparel, furniture, food, toys and jewelry. Amazon is one of the biggest companies in the world right now, and has the highest brand valuation among all its competitors; these companies are collectively known as FAANG. The primary reason for the meteoric rise of Amazon has always been amazing technological innovation and distribution on a massive scale. It has disrupted several major players in the market since its inception.

If this has motivated you to work for Amazon in the near future, do read on and learn how you can get started with the preparation for interviews at Amazon, especially with the data science division, which is at the forefront of technical analysis and innovation in recent times. In this article, we have covered what it is like to face a data scientist interview at Amazon and we have gone through the various sections of the interview as well. We have sourced the data from various job search boards and websites such as Glassdoor, Team Blind app, Indeed and Reddit. This gives us an unbiased view of how the questions and discussions take place during the data scientist interviews at Amazon.

Methodology and Analysis of Amazon Data Scientist Interview Questions

Previously, we have seen and researched interview questions for more than 80 different companies across the data science domain. Building upon this, we shall delve deeper into Amazon itself and discover the different interview aspects and the various types of expected Amazon data scientist interview questions.

Working as a Data Scientist at Amazon is definitely pretty challenging, but look no further, as we have your back here at StrataScratch and will help you build a great career in data science. We have compiled more than 60 interview questions over the past 4 years across various interviews held at Amazon. The data itself has been sourced from job search boards and websites such as Glassdoor, Team Blind app, Indeed and Reddit, with several interviewees sharing what it was like to attend an interview there.

The chart above gives the complete analysis of all the different types of Amazon data scientist interview questions. A quick glance shows us that coding and algorithms are major requirements for the company; this is definitely expected as such technical skills are quite widely used across all divisions at Amazon.

We can also observe that there is quite a lot of importance given to behavioral skills and modeling skills as well. This emphasizes the fact that Amazon has basically stepped into all walks of life, and would therefore require “people persons” to be an essential part of their workforce.

Finally, we also notice that there is not a lot of importance given to system design, statistics and technical theory based questions. While these concepts are mostly used in theoretical sciences, practical applications and essential skills that are necessary for a data scientist usually do not pay much heed to these concepts.

Nevertheless, it is always a good idea to practice as much as you can before heading into the data scientist interview. We will now look at the different types of Amazon data scientist interview questions in detail. We will also see how the data scientist interviews take place at Amazon.

Tips and Tricks to Ace the Amazon Data Scientist Interview

In this section, we shall discuss a few tips and tricks that you can use to prepare efficiently for your Amazon data scientist interview.

  • Learn and practice window functions and the related functions (aggregates, rankings, generation of statistics) thoroughly. These are asked quite often in the coding rounds.
  • Building on the previous point, practice joins and the various forms of joins (left, right, inner, outer) by combining multiple tables and seeing the outputs. Joins are always necessary for SQL coding interviews.
  • Thirdly, learn how to handle time series data and manipulate date and time ranges, as these can be directly asked in questions involving orders and shipping.
  • Other than these technical aspects, it is recommended for you to brush up on the fundamentals of data structures, algorithms and statistics. These will aid you in the theory parts of the interview.
  • Finally, behavioral questions require you to answer honestly and straight to the point; thus, ensure that you go through your resume multiple times and properly know what you have written in it.

Data Scientist Interviews at Amazon

The Amazon data scientist interview process is extremely thorough; however, this is not really surprising, considering the fact that it is one of the biggest organizations in the world. There are various stages of the data scientist interview that actually take place at Amazon. These include an initial online application that is submitted by the candidate, which is followed by a screening assessment. The next step is a telephonic interview or an in-person interview, which further consists of several rounds.

In the case of such thorough interviews, it is always recommended to practice well in advance and go through all the necessary concepts that are required for the data science job. Fortunately, we have already covered some useful tips regarding the same, so make sure to check that out before moving on!

The main positions for which the recruitment takes place in the data science division at Amazon are - Data Scientist, Analyst and Data Engineer. These three roles utilize similar concepts and types of questions, which we will discuss in the upcoming sections of this article.

Regarding the breakdown of the various types of Amazon data scientist interview questions, the chart in the previous section gives a complete description of the same, highlighting the major sections which have massive percentages.

We can observe that almost half of the chart is taken up by coding! A total of 53% of all the Amazon data scientist interview questions is consumed by coding and algorithms together, which are the most essential technical concepts that are asked in tech companies, especially in the FAANG companies.

Modeling and behavioral questions also take up a chunk of the chart’s space, which leads us to our next analysis that there is quite a bit of communication and involvement that is necessary for a data scientist at Amazon.

Furthermore, there are smaller parts which are taken up by technical, statistics and system design based questions. These account for approximately 11% of the total number of questions and are likely to be prerequisite theoretical concepts which would eventually lead to applications or analysis.


Algorithms are a finite sequence of well-defined instructions that involve utilizing data structures and related concepts in order to find approaches and solutions to various questions. And as you would have expected, algorithms definitely play a major role in the Amazon Data Scientist interviews.

According to the above chart, we can see that approximately 12.3% of the total number of questions are based on algorithms. This is a massive number, and employees who are good in this aspect are vital to any tech organization.

A few examples of algorithm-based questions are as follows:

  • “You have an array of integers and you want to find a certain element, what effective algorithm would you use and what is the efficiency of these?”
  • “For a long sorted list and a short (4 element) sorted list, what algorithm would you use to search the long list for the 4 elements? How would the algorithm above scale?”
  • “Given an array of time intervals, find the first "free" time window.”

These types of data scientist questions require a thorough understanding of algorithms and the associated concepts. You would also benefit greatly by learning how to optimize your algorithms, both on the basis of time and space complexities. You can definitely expect these data scientist questions to be of high difficulty, so ample practice is necessary. LeetCode is a great resource to learn, understand and practise algorithm-based questions.


Amazon is particularly known for its interest in hiring people who are really good at coding, as it is a tech company after all! These questions are usually based on SQL and Python for data scientist interviews, but you can expect software development based questions to be asked as well.

The biggest proportion of all the questions asked in Amazon Data Scientist interviews belong to coding questions, as can be seen in the chart above. Approximately 41.5% of all the questions asked are coding questions. Combine this with algorithms, and you get approximately 53.8% of questions which are purely based on your technical skills.

A few examples of coding based questions are given below:

  • “Find the total cost of each customer’s orders. Output customer’s id, first name, and the total order cost. Order records by the customer's first name alphabetically.”

A possible answer to this question can be found here on our platform.

  • “Find employees from Arizona, California, and Hawaii while making sure to output all employees from each city. Output column headers should be Arizona, California, and Hawaii.”

A possible answer to this question can be found here on our platform.

Now, let us discuss a coding question, the approach to solving it, and the solution in detail. The question is given as follows, and can be found here on our platform too.

“Find the percentage of the total spend a customer spent on each order. Output the customer’s first name, order details, and percentage of their total spend for each order transaction rounded to the nearest whole number. Assume each customer has a unique first name (i.e., there is only 1 customer named Karen in the dataset).
For simplicity, let’s just assume that the ‘order_cost’ represents the total cost of the user’s order for that particular transaction record (i.e., ‘order_cost’ does not represent the unit cost of the item)”.

At first glance, we notice that our main aim in this Amazon data scientist interview question is to find the total percentage of the amount spent by a customer during his/her transaction which involves multiple items.

We assume a couple of things here - each customer has to have a unique first name and secondly, the total cost of the user’s order for a particular transaction would be called the order cost, and not for a single item in that transaction.

The main approach with which we can solve this coding data scientist question is given below:

  • First join the ‘customer’ table with the ‘orders’ table using an inner join. Any customers that did not place an order will be removed in the resulting dataset.
  • You’ll want to find the customer’s total amount of purchases and then divide the cost of each individual order with the total amount across all orders.
  • To calculate the total amount across all orders for each customer, use a window function and partition the window by the first name of the customer.
  • For each transaction, take the cost of the order and divide it by the total amount for each customer.
  • Round the percentage to the nearest whole number using the round() function.

Finally, the complete SQL solution to the above Amazon data science interview question is as follows:

    round((o.order_cost / sum(o.order_cost) over (PARTITION BY c.first_name)::
    float) * 100) AS percentage_total_cost
FROM orders o
JOIN customers c ON = o.cust_id

These data science coding questions require a lot of practice to get right on the first attempt, thus we recommend you to go through the various questions and practise them as much as you can before attempting the Amazon Data Scientist interview.

Technical Concepts Tested

Now let us discuss the technical concepts that are usually the main focus of these coding questions in the data science interviews.

Window Functions

Right off the bat, we noticed that there is quite a bit of emphasis given on utilizing window functions in arriving at the solutions. These functions are predominantly used in SQL to perform calculations across a set of rows that are related to your current row.

One such coding question is given below:

“Given a table of purchases by date, calculate the month-over-month percentage change in revenue. The output should include the year-month date (YYYY-MM) and percentage change, rounded to the 2nd decimal point, and sorted from the beginning of the year to the end of the year. The percentage change column will be populated from the 2nd month forward and can be calculated as ((this month's revenue - last month's revenue) / last month's revenue)*100.”

A possible solution to this question can be found here on our platform.


These types of questions are most commonly asked in data science interviews. Joins require quite a bit of practice to get them right, so we recommend you to check out a few examples and practice them as much as possible.

An example of a coding question that utilizes joins is given below:

“Find ‘favorite’ customers based on the order count and the total cost of orders. A customer is considered as a favorite if he or she has placed more than 3 orders and with the total cost of orders more than $100. The ‘order_cost’ column is the total cost of the order. Output the customer's first name, city, number of orders, and total cost of orders.”

A possible solution to the above question can be found here on our platform.

Date and Time Ranges

Finally, we also observed a lot of questions being asked on utilization and/or manipulation of dates and time ranges. These types of questions require you to calculate or find some data within a given range of dates or specific times, within those dates.

One such example is given as follows:

“Find the customer with the highest total order cost between 2019-02-01 to 2019-05-01. Output their first name, total cost of their items, and the date. For simplicity, you can assume that every first name in the dataset is unique.”

A possible solution to this question is given here on our platform.

To sum it up, a lot of importance is given to window functions and their related functions in the Amazon Data Science interviews. This is followed by joins and other questions based on date and time ranges.


Based on our analysis, modeling-based questions in the Amazon data science interviews are usually asked on machine learning concepts. You would have to learn and understand how machine learning models work, the different approaches such as supervised and unsupervised learning, regression and so on. The mathematics involved in all these concepts may also be asked in the data science interviews.

It can be observed from the above chart that approximately 13.8% of all the questions account for modeling based questions. This is quite a big number when it comes to a data science interview, as you would most likely be required to implement all these in one form or another, if and when you eventually work for Amazon.

A few examples of these modeling-based questions that are asked in Amazon are given below:

  • “What are the supervised machine learning techniques that you know about?”

A possible answer to this question can be found here on our platform.

  • “If you have a customer and you know 1. where they live, 2. their income, 3. their gender, 4. their profession, how would you define a machine learning algorithm that predicts whether they will “buy today” or “not buy today”?”

A possible answer to this question can be found here on our platform.​

  • “How does a neural network with one layer and one input and output compare to logistic regression?”

A possible answer to this question can be found here on our platform.​

You can easily solve/answer these types of questions by thoroughly understanding the basic concepts involved in machine learning and statistics.


Technical questions are those questions which focus mainly on the fundamental concepts of data structures and algorithms. These concepts will eventually be utilized to build large applications and optimize them for storage and speed.

We can see from the above chart that technical questions are one of the least asked questions in the Amazon Data Scientist interviews, accounting to around 3.1% of the total number of questions. This is due to the fact that the applications of these fundamental concepts will be asked in the algorithms and coding parts of the interview anyway.

A couple of examples of technical questions are given below:

  • “What is the difference between a linked list and an array?”

A possible answer to this question can be found here on our platform.

  • “What is the difference between a stack and a queue?”

Technical questions are some of the simplest questions that you can expect in the Amazon Data Scientist interview. So ensure that you prepare well and study the fundamental concepts thoroughly.


Questions based on statistics are usually based on concepts involving data science and data analysis. A good understanding of the theory and principles involved in statistics will definitely help you in this aspect of the interviews.

The above chart shows us that approximately 4.6% of the total number of questions are based on statistics. Even though this is a sparse number, it is always better to learn the statistical concepts, as it may be useful when it comes to coding.

A few examples of statistics-based questions are given below:

  • “In an A/B test, how can you check if the assignment to the various buckets was truly random?”

A possible answer to this question can be found here on our platform.

  • “How would you explain to an engineer how to interpret a p-value?”

A possible answer to this question can be found here on our platform.​

  • “How do you treat collinearity in data analysis?”

A possible answer to this question can be found here on our platform.​

As you can see, these questions can be easily tackled by understanding statistical methods and principles which are usually used in data science. Thus, it would be a good idea to learn and practice them as much as you can.


Behavioral and miscellaneous questions are quite common in Amazon Data Scientist interviews. It helps the recruiters understand how well you can communicate and interact with other members in a team. Being a data scientist means that you will have to work with various other people and this is definitely a part of the Amazon principles.

It can be observed from the above chart that quite a large amount of questions is covered under the behavioral category, accounting for around 21.5% of the total number of questions in the Amazon Data Science interview. These questions are usually based on what you have already worked on, your experiences, and other related questions.

A few examples of behavioral questions are given below:

  • “Tell me about your experience.”
  • “Describe a time that you have a different opinion with colleagues.”
  • “Tell me about a time you faced a crisis at work. How did you handle it?”

As we can see in the above questions, most of these are based on previous experiences and general encounters one would face when working in an organization. Answering these questions with a bit of enthusiasm will always help the recruiter better gauge your communication skills and will also show your interest towards the company itself.

System Design

Last but not the least, questions based on system design are also frequently asked in the Amazon Data Science interviews. These types of questions require a good understanding of how a system is designed, built, tested, packaged and released.

We can see in the above chart that approximately 3.1% of the total number of questions asked in the Amazon data science interviews are system design based questions. Some aspects of database schema, data mining and warehousing can also be asked here.

A few examples of such questions based on system design are as follows:

  • “Design a schema. What is SCD?”
  • “What are the pros of Star schema?”
  • “Design DWH for the support team which manages tickets.”
  • “Design DWH for gathering statistics about music streaming services.”

Tackling these types of questions requires some basic knowledge of database management systems, data mining and data warehousing. Ensure that you brush up on these concepts before heading into your interviews.


Amazon is one of the largest companies in the world, spanning across multiple domains and industries. Working for such a huge company has its own benefits and requirements as well. Therefore, the amount of preparation required is also proportional to the quality of tech that is output by Amazon.

The company focuses heavily on coding and algorithms, and it is a good idea to practice SQL and related concepts thoroughly. Several coding and non-coding questions from previous Amazon interviews are also available on our platform, which can be utilized effectively in your preparation for the interviews.

Communication also plays a vital role in securing your dream job at Amazon, as there are quite a lot of behavioral questions which are asked in the interviews. Ensure that you speak confidently and be honest when giving your answers to the interviewer.

We hope that this guide has helped you in your preparation for the Amazon data scientist interview. We urge you to practice well for your interview and we wish you all the best!