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Amazon Data Scientist Internship A Complete Guide
Data Science

Amazon Data Scientist Internship: A Complete Guide

6 minute read | July 8, 2020
Sakshi Gupta

Written by:
Sakshi Gupta

Ready to launch your career?

Amazon accepted more than 8,000 interns in 2020, of which 8% were in data science and applied science. As one of the biggest data-driven companies in the world, its need for data science specialists rivals the likes of Facebook, Google, Apple, and Netflix—it employs data scientists, machine learning engineers, and data engineers across its retail business, Amazon Web Services (AWS), its virtual assistant Alexa, its production studios, and in the management of its growing workforce, which includes both technical workers and those who keep its supply chain running.

The company offers internships in two main tracks: technical engineering and research and business.

  • The former encompasses areas such as software development, hardware development, applied science, product development, data science, and research science
  • The latter includes operations management, sales and marketing, product management, program management, and retail/consumer leadership

Amazon boasts that its interns get to work on projects that actually see the light of day, whether it’s a customer-facing product like building new features for, or an internal product for improving the order fulfillment process. It also promotes that every intern is paired with a mentor and a manager to guide their development over the course of the internship and that supplemental training is offered in the form of classroom education and self-service resources.

Amazon offers some of the technology industry’s most coveted and highly paid internships, many of which lead to permanent, full-time positions. Read on to learn more about Amazon’s data science internships and how to get a foot in the door.

What does an intern at Amazon do?

The skills and experience levels of data science interns vary depending on the kind of internship undertaken. For example, in a listing for a data science intern, Amazon requires that candidates be pursuing a Master’s or equivalent advanced degree from a top-tier technology school, with preferred qualifications in computer science, computer engineering, statistics, mathematics, or a related technical discipline. A separate listing for a research scientist in its robotics division requires that candidates have a similar background to that of its data science applicants, in addition to enrollment in a Ph.D. program and at least two years of relevant academic research experience.

As far as minimum qualifications go, though, data science interns are expected to be familiar with programming languages such as SQL, Python, and Java, and scripting languages such as PHP or Perl. A knack for algorithm analysis, computational complexity, and creative problem-solving also doesn’t hurt.

With a sufficient technical foundation, Amazon’s data science interns are thrust into the deep end and work on real-world projects. When Na Zhang was an Amazon intern on the machine learning team, she worked on deep neural networks to identify fraudulent transactions and got to see first-hand how a company as large as Amazon handles terabytes of data and makes its systems more scalable. Boris Marchenko, who has worked alongside interns at Amazon, said that interns contribute directly to the main codebase and that, in addition to conducting their own research, they’re expected to implement their findings.

“We have two goals for our interns,” said Xin Luna Dong, a principal scientist at Amazon. “One is that they publish their work in top-tier conferences, and the other is that their work will eventually make it into production.”

According to Dong, eight out of ten of the 2019 intern projects resulted in a published research paper, and five of those projects have moved toward production.

Other common responsibilities of Amazon’s data science interns include using data analyses and statistical techniques to help the company guide its decision making, assisting in forecasting and prediction, collaborating with multiple teams while leading quantitative analysis in the development of solutions, and analyzing and solving business problems at their root.

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What is the process to get a data science internship at Amazon?

Landing an internship at Amazon requires showing the application reviewers that you have both the technical chops to handle database querying challenges and the right attitude to deliver on its 14 leadership principles.

Below are steps to follow that can bring you closer to an Amazon internship.

  • Step 1: Build on your skills.

A baseline requirement for qualified applicants is knowledge of programming languages such as SQL or Python, and some experience in solving analytical problems using quantitative approaches (or equivalent). If your prior degree-program didn’t help you develop these skills, or you’re rusty, consider doing a refresher through independent study or a mentor-supported bootcamp.

  • Step 2: Build that CV and portfolio

While Amazon’s internship program lists many preferred qualifications, a strong CV and accompanying portfolio will help hiring managers see whether you have the drive and initiative to solve problems and complete projects. If you’ve contributed to research or worked with professors or graduate students on projects, share it. If you’ve worked on your own projects by wrangling publicly available data sets, document it.

Aashish Jain, an Amazon research scientist who initially landed a job at Amazon as a data scientist, said that during his application process, hiring managers were interested not only in what skills he had, they wanted to get a sense of how he approached problem-solving by looking at his prior projects.

“They look at how you would work if you were given a problem,” Jain said. “They also want to know what you have done in the past because that tells them about your problem-solving skills.”

While Jain was interviewing for a full-time role instead of an internship, the same principle applies—if you’re competing against thousands of applicants for a role, a strong portfolio will set your application apart.

  • Step 3: Consider the leadership principles

Amazon takes its 14 leadership principles seriously. In addition to evangelizing them within the organization, the principles—which include customer obsession, ownership, learning and being curious, insisting on the highest standards, thinking big, and diving deep—also come up during the hiring process, according to current and former interns and employees. When choosing projects and case studies to include in a portfolio, consider how they demonstrate an understanding or a practice of the 14 leadership principles. When preparing for the internship interview itself, consider how your responses and the narrative you craft about yourself and your experiences best reflect those principles.

  • Step 4: Shine in the interview

If you make it to the first general interview, which is typically conducted over the phone, then you want to be prepared on multiple fronts: showing that you are deeply familiar with and understand Amazon’s mission and a suite of products, and being able to discuss your background and previous work.

If you make it to subsequent rounds, expect to be tested on your technical skills—former interns have had to use SQL and Python to provide solutions to product analytics questions, and have also been tested on their knowledge of statistics, architecting machine learning solutions, and architecting A/B testing solutions. You can also expect behavioral questions related to the leadership principles.

Are interns working in data science at Amazon paid?

Amazon interns are among the most highly paid in the technology industry. The median monthly intern salary is $7,725, according to Glassdoor.

Given that “frugality” is one of the company’s leadership principles, though, Amazon offers fewer perks and benefits than some of its competitors. Former interns have shared that they received a housing stipend and, for those who work from the company’s Seattle campus, a transportation card. Beyond that, most of the benefits are related to career development and training, with the company hosting intern-specific events, offering formal training opportunities, and a supportive team environment.

What’s it like to be an intern in data science at Amazon?

The experience of being a data science intern varies depending on the team with which an intern works. That said, data science interns at Amazon commonly report that they get to work on projects that end up going into production. For example, Meghana Palukuri, a Ph.D. student in computational science, engineering, and mathematics said that during her internship at Amazon, she delved deeper into natural language processing, and for her project, she built a product embedding space for making substitute product recommendations. During the company’s Alexa Skills Hackathon, Palukuri also worked with four other interns to build an “intern chat” for Alexa to enhance the pre-onboarding experience for future interns.

Amazon interns also get access to a variety of workshops that are relevant to their disciplines, can attend panels and speaker series that are also attended by full-time employees, and are encouraged to connect with fellow interns in their cohorts to establish a sense of community.

“I built connections with other interns, listened to their experiences during the internship, and learned about the research projects they were working on both at university and Amazon,” said Alesia Chernikova, who was an Amazon intern in 2020. “It was beneficial for me to look at things not only from the inside, but also understand it from the perspectives of others.”

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.