Netflix is a streaming media company headquartered in Los Gatos, California, that has permeated culture as the largest content media company disrupted by tech. Founded in 1997, Netflix started out as a DVD rental service and then expanded to the streaming business. Now Netflix has over 150 million paid subscriptions worldwide, including its 60 million US users. With streaming support on over a thousand devices and around 3 billion hours watched every month, data is collected on over 100 billion events per day.
Data science is in the DNA of Netflix, and Netflix leverages data science to improve every aspect of the user experience. Netflix has, over the years, been leveraging data science for its content recommendation engine to decide which movies and TV shows to produce and to improve user experience.
This guide provides an overview of potential questions you might encounter in a Netflix data scientist interview, spanning various topics, along with some tips to help you excel.
The role of a data scientist at Netflix is heavily determined by the team. However, general data scientist roles at Netflix cuts across business analytics, statistical modeling, machine learning, and deep learning implementation. Netflix is a large company that has data scientists working in over 30 different teams including personalization and algorithms, marketing analytics team, and the product research and tooling team, with skillsets ranging from basic analytics to heavy machine learning algorithms.
Netflix hires only qualified data scientist with at least five years of relevant experience. Their requirements are very specific and are recruiters are keen to hire specifically for each job role. It helps to have specific industry experience specific to the role on the team.
Other relevant qualifications include:
The term data science at Netflix encompasses a wide scope of fields and titles related to data science. The title data scientist comprises of roles and functions that span from product analytics-focused data scientists to data engineering and machine learning functions.
Personalization Algorithms: Collaborate with product and engineering teams to evaluate the performance and optimize personalization algorithms used to suggest movies, TV shows, artwork, and trailers to Netflix members.
Member UI Data Science and Engineering: Leveraging custom machine learning models to optimize the user experience of the product for all subscribers.
Product Research and Tooling: Developing and implementing methods to advance experimentation at Netflix at scale. This involves developing data visualization frameworks, tools, and analytics applications that provide other teams with insights into member behavior and product performance.
Growth Data Science and Engineering: Focus on growing the subscriber base by building and designing highly scalable data pipelines and clean datasets around key business metrics.
Marketing Data Science Engineering: Creating reliable, distributed data pipelines and building intuitive data products that provide stakeholders with means of leveraging data across domains in a self-service manner for all non-technical teams.
Netflix’s Data Scientist interview process is similar to other big tech companies. The process starts with an initial phone screen with a recruiter and then a short hiring manager screen before proceeding to a technical interview. After passing the technical screen, an onsite interview will be scheduled. This interview comprises two parts with 6 or 7 people.
The initial screen at Netflix is a 30-minute phone call with a recruiter. The recruiters at Netflix are highly specialized and very technical. Their job is to understand your resume and see if your past experience, projects, and skillset matches up to the role. The second point of this part of the interview is to test your general communication skills and explain the role and its background to you.
Next is the hiring manager interview. This one will focus more on past experience and dive into more of the technical portion of what you’ve done within data science and machine learning. While the recruiter gets a sense of your projects at a high level to fit with the team, the hiring manager will ask you more in-depth questions like why you used certain algorithms for a project or how you built different machine learning or analytics systems.
The hiring manager will also get to tell you more about the roles and responsibilities of the team. Note that Netflix is big on culture and values, and you may be asked to pick a value and explain how best it suits you.
After passing the initial screening, the technical screen is the next step in the interview. This interview is usually 45 minutes long, and it involves technical questions that span across SQL, experimentation and ab testing, and machine learning technical questions.
The onsite interview is the last stage in the interview process, and it comprises two-part interviews with a lunch break in between. If you’re from out of state, Netflix will fly you out to Los Altos or Los Angeles for the on-site, and you’ll first meet with the recruiter to go over the interview.
It involves one-on-one interviews with 6 or 7 people including data scientist team members, team managers, and a product manager. The Netflix onsite interview is a combination of product, machine learning, and various analytical concepts. This round will comprise of data science interview questions around product sense, statistics including A/B testing (hypothesis testing), SQL and Python coding, experimental and metric design, and culture fit. If the role is more focused on engineering, expect more machine learning and possibly deep learning interview questions.
Here are some examples of Netflix’s Data Scientist interview questions:
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