Amazon is considered one of the top workplaces for data scientists, offering both competitive salaries and exciting opportunities to shape products and services used by hundreds of millions of people worldwide. Read on to learn more about what it’s like to be a data scientist at Amazon.
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If there’s one company where a data scientist is unlikely to get bored, it’s Amazon. With a business that spans online retail, virtual assistants, cloud-based web services, entertainment, and subsidiaries that cover podcasting, ebooks, and grocery chains, Amazon relies on data scientists to gather, process, and tease out business insights. It’s an organization where a data science specialist can have a large-scale business impact, highlighting useful findings and developing data-driven solutions that can change the course of the company.
Read on to find out how to get hired as a data scientist at Amazon—and don't forget to check out the guides below!
Whether you’re a data scientist helping Amazon forecast product sales, training Alexa to recognize human speech, developing machine learning models for Amazon Web Services (AWS), or finding ways to make the company’s warehouses more efficient, Amazon expects all its data scientists to strive toward its 14 leadership principles, according to current employees and interns.
Some of the principles include “customer obsession,” “ownership, “learn and be curious,” and “insist on the highest standards.”
“With something like ‘ownership,’ it means taking the initiative and not relying on others to come up with ideas for you,” said Aashish Jain, a Springboard alumnus who joined Amazon as a data scientist and has since advanced to the role of a research scientist. “With a principle like ‘dive deep’, it means we don’t look at things superficially and we really care about attention to detail.”
According to Jain, these principles inform how data scientists and research scientists at Amazon work. And while the principles inspire a culture of hard work, Jain said that the hours are flexible—he typically works a 9-5 day, although some of his colleagues start as early as 7:30am and finish by 3:30pm, while others start and end their days much later.
Amazon doesn’t boast playground-like campuses around the world that include complimentary restaurants, cafes, or laundry services, but it does offer its employees generous compensation packages that often include cash bonuses and stock options. Amazon employees also receive a slew of other benefits:
The base salary range for an Amazon data scientist depends on years of experience, education, and location, and the total compensation can vary greatly depending on whether someone qualifies for an annual bonus or stock grants.
At the most entry-level of the range, Amazon’s data science interns make around $7,725 a month, in addition to benefits such as a housing stipend and public transit card.
Data scientists who hold an undergraduate degree in a relevant field such as computer science, statistics, or mathematics, and have a few years of experience under their belt can earn around $114,357, in addition to cash bonuses and stock grants.
Senior data scientists who also hold masters' degrees or a Ph.D. in a related field, such as machine learning, can make around $180,000. The longer a data scientist stays with Amazon, the higher the likelihood that they will receive enough stock grants to rival their base salary.
Check out this page for more information about salary and benefits for data scientists at Amazon.
Like many technology companies, Amazon’s interview process for data scientists involves both technical and behavioral screeners that involve a mix of phone and in-person interviews.
The technical components of the interview include measuring a candidate’s proficiency with programming languages such as Python, Java, and SQL; ensuring that a candidate has a strong understanding of statistics, mathematics, data mining and data extraction, and the complete data pipeline; and checking a candidate’s familiarity with machine learning and data visualization tools.
On the behavioral front, the company wants to get to know a candidate; assess their communication skills; evaluate their approaches to problem-solving, and see examples of the 14 leadership principles in action.
“Different teams have different requirements,” said Amazon research scientist Aashish Jain, who added that hiring managers will administer technical quizzes during an interview and also look at portfolio work. “They look at how you would work if you were given a problem. They also want to know what you have done in the past because that tells them about your problem-solving skills. They dig into both.”
Successful candidates typically hold basic qualifications such as a bachelor’s degree in computer science, mathematics, statistics, or a related field, as well as a strong portfolio that shows experience working with data pipelines and creative problem-solving.
Amazon accepted 8,000 interns in 2020 across two tracks: technical 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.
Potential interns submit applications through Amazon’s portal and, if their CVs make the cut, proceed to an interview process where they are expected to demonstrate technical knowledge of coding and scripting languages, creative problem-solving skills, and an attitude that reflects the company’s leadership principles.
While the company expects a lot of its interns upfront, former interns have said that Amazon offers them significant support in the form of workshops, panels, mentorship, and formal classroom training to allows them to further develop their skills and build relationships with their peers.
"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.”
The day-to-day work of an Amazon data scientist varies by team. For example, data scientists focused on business forecasting might devote most of their time to cleaning and digging into datasets on product sales and shopping trends, while data scientists working on Amazon’s Alexa might spend more time on machine learning algorithms, natural language processing, and deep learning.
For Aashish Jain, who works on the team that focuses on Alexa, an average week involves sprint planning meetings, team meetings, and weekly retrospective meetings. When he’s not in a meeting, Jain builds different machine learning models—a process that requires him to read research papers, identify which algorithms work best for the problem he’s trying to solve, and implement them using Python. “It’s not just model building,” he said. “There’s data analysis, data cleaning—it’s the whole data science pipeline.”
Jain credits Springboard’s Data Science Career Track capstone project for both preparing him for his role at Amazon, and for helping him demonstrate to hiring managers his knowledge of the data science pipeline.
In the lead-up to his interview with Amazon, Jain said he spent a lot of time working on smaller projects in order to learn and refresh himself on the different data science skills he might need. But he later realized that one big project—such as the capstone project he completed at Springboard—can carry much more weight than lots of little projects.
“Even if you do just one thing, but you do it end-to-end, starting from getting your data, defining your problem, data cleaning, developing a model, evaluating that model, analyzing—everything—if you do it well, I think that boosts your confidence and shows that you know the data science pipeline very well,” he said.
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