Data scientists have been high in demand for the past several years, as some of the world’s biggest technology companies seek to maximize the power of data-driven strategies. Salaries for data scientists reflect this demand; the job has a median base salary of $120,000, and more than 10,000 job openings, according to figures from Glassdoor’s best jobs list.
How to become a data scientist at a Big Tech companyBY Meghan MalasMay 11, 2022, 1:44 PM
Companies like Amazon Web Services (AWS) are hiring an increasing number of graduates with degrees in data science and machine learning for the Amazon machine learning solutions lab team, says Antonia Schulze, a data scientist at AWS. And more graduates of data science degree programs will likely be hired as a growing number of universities offer specialized programs in data science, Schulze explains.
At AWS, at least, it’s most common for data scientists to have degrees in computer science and mathematics or statistics. And while education is a key component, there are other qualities besides a specific degree that are important for data scientists in Big Tech.
Fortune spoke with three experts at AWS, Netflix, and Meta to learn how to become a data scientist at a Big Tech company.
Big Tech companies prefer applicants who have a master’s degree in data science
In the past decade alone, MIT, the University of California–Berkeley, New York University, and Yale University are among schools that have established centers, institutes, departments, and divisions dedicated to data science. This is an indication that higher education institutions see a need for more specialized programs to prepare graduates for the field.
Specific technical knowledge is needed to become a data scientist, and a master’s degree in a quantitative field—while not always required—is a good way to upgrade your skills.
“In terms of hard skills, programming knowledge—specifically R and Python—is essential,” Schulze says. “However, a basic understanding of mathematical concepts supporting data science and machine learning models is an imperative too.”
The majority of data scientists at Netflix have a master’s degree or a Ph.D. in a field such as statistics, machine learning, economics, or physics, Stone says. These types of degree programs provide students with the technical skills in data analytics, machine learning, statistics, or causal inference that Netflix requires from its data scientists. Meta requires all applicants in data science roles to have a bachelor’s degree in mathematics, statistics, or a relevant technical field. A master’s or Ph.D. in a quantitative field is a preferred qualification at Meta as well.
But while an advanced degree may provide the upskilling you need to get a job at AWS, it’s not everything. “An academic structure can certainly help in the way we approach scientific problems, however, all of that can be learned on the job too,” Schulze says.
Big Tech firms prioritize quality over quantity for work experience
“While we often see candidates with advanced degrees apply, what is equally or more of an interest to us is prior work experience,” Stone says.
At Netflix, while a strong technical foundation is necessary, it is also important that data scientists are creative in how they utilize data achieve better business outcomes. Additionally, for some data roles, expertise in entertainment and studio production may be needed. At Meta, data scientists need to demonstrate experience with measuring success of product efforts, as well as an ability to forecast key product metrics to understand trends.
But you don’t necessarily need several years of experience in the field to land a data science job at a Big Tech company. Professionals on the data science teams at Netflix and AWS have anywhere from a couple years to decades of work experience before joining.
“When I graduated, I joined the Amazon operations team as a business intelligence and data science intern,” Schulze says. “Upon completion of my internship in 2019, I got the opportunity to join AWS as a data scientist with the Machine Learning Solutions Lab and have been with the same team since joining full-time.”
Successful Big Tech data scientists are dynamic, connect data to big picture
“Leaders give context on business priorities and strategy, but individual contributors such as data scientists drive most of the details on both the ‘what’ and the ‘how,’” Stone tells Fortune. Netflix’s team has grown steadily over time and now includes collaborating data scientists, data engineers, data analysts, and consumer researchers..
This collaboration relies on team members having strong communication skills. At companies like AWS, Netflix, and Meta, data scientists need to be able to share information and ideas effectively with other stakeholders, including people without technical backgrounds. Data science is a rapidly evolving field, so tech companies seek employees with the ability to translate data to business impact without a predefined roadmap.
“In order to grow and thrive in this evolving scientific space, data scientists need to enjoy learning and researching new topics constantly,” Schulze says.