Netflix Data Scientist Interview Guide

tl;dr

Netflix stands out among FAANG companies for its small size, global impact, and reputation. Their goal of keeping subscribers and audiences entertained has taken them from a physical media delivery service to one of the most popular media streaming platforms in the world. Along the way, they’ve developed and invested in a well-documented organizational philosophy and culture. Historically, they’ve favored hiring senior employees to make up their relatively small, highly performing teams, although there is some indication that this approach is changing.

One of the key differentiators for Netflix is their extensive use of data for everything from recommending new shows to viewers to researching market trends and even testing security issues. Like other tech companies, data is central to many, if not all, of their major decisions. As with other roles, Netflix primarily hires senior data scientists who already have both academic and professional experience. Once a data scientist is embedded in a team, they’ll work with other stakeholders to analyze data and assess outcomes.

Netflix is famous for having a particular set of cultural values, in particular flat-org structures prizing individual responsibility and accountability, high levels of dedication, and deep professional knowledge and skills. This approach has enabled them to grow into a global business despite having 1/6th the staff size of other FAANG companies. If you enjoy working collaboratively as a data scientist, giving and receiving constructive criticism, and are at your best when granted a high level of autonomy, Netflix might be a good place for you.

What does a Netflix Data Scientist do?

Because data plays such a key role at Netflix, working as a data scientist could position you as a crucial team member in nearly every department at the organization. Netflix Data Scientists work in discrete teams and projects, and have particular mandates to collaborate across teams and disciplines to make data accessible, usable, and useful to all stakeholders. They’re also called upon frequently to analyze data for insights and inference, helping the teams make difficult choices that drive the organization forward. Some of the teams Netflix Data Scientists work in include:

• Content
• Finance
• Marketing and sales
• Security
• Games
• Personalized data

A content DS, for example, would build tools to help content teams make informed decisions about what content is most effective for particular audiences. They might develop a system to track or infer performance based on different metrics, visualize the results in a way that is accessible and actionable for other non-DS stakeholders, and provide and source feedback in order to identify new means to improve data usability.

No matter which team you’re on, as a Netflix DS you’ll be expected to explain and defend your methods and work, working cross-functionally to come up with solutions to problems and incorporate new approaches and feedback that will lead to better outcomes. Being able to communicate and explain your process is a key value for Netflix employees.

Compensation depends on your background and performance during the interview process, but here are the listed yearly compensation levels for Netflix Data Scientists:

• L4: $339,000
• L5: $505,000
• L6: $721,000

Before you apply

• Research Netflix data science projects and organizational philosophy: Netflix has an organizational approach that avoids overreliance on process, instead favoring direct collaboration and experimentation.
• Learn as much as you can about causal inference: A major point of emphasis for all Netflix Data Scientists is a deep knowledge of causal inference. Being able to speak to and utilize different approaches to a data problem or question is important, but you’ll need to show an ability to use causal inference methodologies to account for missing data or situations where you can’t easily A/B test.
• Learn about Netflix’s culture and apply it to your experience: In addition to testing your skills, the interviewers will also assess your understanding and compatibility with Netflix’s stated values and culture. You can prepare by reviewing your work history and highlighting times when you were required to defend your work, make a difficult decision, or when you thrived with a good deal of autonomy.
• Get feedback from interviewers familiar with Netflix. They’ll walk you through the process and give you advice and feedback to help you approach the real interview with confidence.

Interview process

The Netflix Data Scientist interview process has been subject to a significant set of changes as the organization has grown. Although Netflix has built their success on hiring primarily senior and high-level subject matter experts, their size and scale has led to more centralization and process-driven approaches, including hiring. The DS hiring process typically includes ~7 conversations:

1. Recruiter screening, focused on general fit and culture-fit probing
2. General technical screening, focused on DS fundamentals
3. Team-specific screening, including a practical problem this team faces
4. Onsite interview, including rounds on:
   • Causal inference and experimentation
   • Coding assessment
   • Product and subject matter
   • Culture and leadership interview

Recruiter screening

This interview takes place over the phone, and primarily focuses on your experience and culture fit. The recruiter will ask you about your education, work experience, and some basic work process and culture-fit questions. This interview will uncover any possible conflicts or red flags, so it’s important that you have a solid understanding of Netflix's culture and values.

Some questions they might ask include:

Why do you want to work at Netflix?

Asked at Netflix 

See question

What do coworkers say about you? Share some positive and negative feedback you've received.

Asked at Netflix 

View 3 answers

How would you improve Netflix?

Asked at Meta, Netflix  •  6 months ago

View 3 answers

Technical screening rounds

General phone screening: This is a newer part of the process, and is a standardized step for all roles. The call will be conducted by a highly experienced and seasoned Netflix DS, and will draw from a pool of questions. The interviewer will not be the team you’re interviewing for, so will focus more on your general level of data science knowledge and experience, particularly your knowledge of causal inference and the process you take to conduct experiments. Your answers and experience will be used to set your DS level.

Some example questions include:

• Tell me about a time the business problem wasn’t clearly defined. How did you handle it?
• How would you measure engagement for a productivity app? What features or behaviors would you track?

Team-specific phone screening: This interview will be much more hands-on, and is conducted by a data scientist either on the team you are applying to or who has some familiarity with that team. You’ll be asked to solve a series of data-focused questions, each designed to test your abilities using SQL, working with data tables, and other DS skills.

The questions will be fairly straightforward to begin with before moving into more complex areas later on. They’ll focus on a problem the team is facing or has faced recently, and the interviewer will expect you to utilize causal inference to inform your approach.

For example, if you’re applying to the personalized data team, you might be presented with a problem that requires you to use SQL to pull viewer engagement statistics from a table of data about a recently-launched streaming product. Because the product was launched before an A/B test was set up, you’ll need to think through methods to infer viewer engagement relative to other streaming products. The set of questions will require you to plan using causal inference and get hands-on with SQL.

Onsite round

Technical assessments

Causal inference and experimentation assessment: As stated above, Netflix puts a very heavy emphasis on causal inference when hiring data scientists. This round will be primarily focused on the different ways you might apply a certain causal inference approach to the problem at hand. A large number of applicants fail this step because they reach for a more “standardized” approach, such as A/B testing, when causal inference is the more efficient and desirable framework.

For example, if you’re applying to work on the marketing and sales team, you might be asked to analyze data on the success of a newly installed billboard ad in a particular city. The interviewer will want to know if you can use the engagement data to make a determination about whether the billboard was successful, and, if so, whether it should be expanded to more cities. In this example, causal inference can be used to make an informed assessment that will be much less cost- and labor-intensive than an A/B test, which would require more billboards to be installed in other cities.

The interviewer will not expect a perfect answer, but will assess your ability to think about and utilize causal inference techniques, such as creating a synthetic control group of a set of cities to measure the inferred results against, or using propensity matching to determine the set of characteristics that could improve or diminish the effects of a billboard campaign in a given city. Your goal should be to show your deep understanding of both the application and value of these tools, and to be able to clearly articulate and defend your process to the interviewer.

You’ll also be asked to discuss and plan some experimentation parameters, for example a problem where you’re conducting multiple A/B tests at once and have to deal with a high level of complexity, or designing a sequential test which automatically demotes underperforming variations. Because of the high level of importance Netflix places on causal inference, this element of the interview will be less important, but it’s important that you prepare heavily for both.

Some example prompts and questions:

Imagine you are a data scientist for Netflix. How would you use data to decide whether a TV series is worth renewing?

Asked at Netflix 

See question

Given stock prices for the next n days, how can you maximize your profit by buying or selling one share per day?

Asked at Adobe, Apple, Capital One + 11 more 

View 19 answers

How would you measure the success of the recommendation engine at Netflix?

Asked at Netflix 

View 3 answers

Identify success metrics for a marketing campaign to get new users, then design an experiment to determine if the campaign should continue.

Asked at Anthropic  •  5 months ago

See question

Coding assessment: These questions will present you with a dataset that needs to be sorted and analyzed using SQL (or possibly Python). You might be given a dataset of users who signed up and subscribed using a particular referral code, and write a query to filter out those who are inactive 30 days after signing up. Once you generate usable data, the interviewer might ask further questions and require more queries.

This round can also lead to more causal inference questions. If you complete the tasks, the interviewer may ask you to think through and design a way to test the performance of the referral program based on the data you saw. For example, you might be asked to draw out a directed acyclic graph, inferring the factors that would lead to a successful expansion of the program.

Some example questions and prompts:

Write a query to find all dates where the stadium had three or more consecutive days with attendance of 100 or more people.

Asked at Amazon 

View 2 answers

Explain the key differences between BETWEEN and HAVING clauses in SQL.

Asked at Deloitte 

View 3 answers

Team alignment interviews

The final step of the onsite round is a series of two or three culture and subject matter interviews, covering the following:

Product and subject matter interview: This interview will focus on your domain knowledge and understanding of the projects the team is responsible for. In addition to showing some domain expertise, you’ll need to identify and discuss areas for collaboration and cross-functional work. For example, if you’re interviewing for the security team, you might discuss how to obtain data from and collaborate with data scientists on the account services, personalized data, and finance teams. Netflix is famous for continually testing their responses to large-scale service failures, which can be another area to discuss.

Culture and leadership interview: In addition to causal inference, showing a solid understanding and alignment with Netflix’s culture and values is a major point of emphasis. To prepare, review Netflix’s cultural documentation, both current and past, in order to understand what they value and what has changed over time. Once you feel you have a good grasp of it, go through your resume and past experiences and identify times where you’ve done or contributed to something that aligns with their values. Common examples are when you said “no” or had to defend your approach, a time when you accepted and utilized constructive feedback from a peer, or when you were given a lot of leeway to complete an assignment and exceeded expectations.

Some questions you might encounter include:

Tell me about a time when you received negative feedback and how you handled it.

Asked at Amazon, American Express, Anthropic + 10 more  •  9 days ago

View 10 answers

Tell me about an impactful project that you led.

Asked at Dropbox, Google Cloud (GCP), JP Morgan Chase 

View 2 answers

How do you handle saying no to stakeholders?

Asked at Netflix 

View 6 answers

Additional resources

• The Netflix Data Science blog
• Get a Job at Netflix: Interview Process and Top Questions
• Top data science interview questions
• Top Netflix interview questions
• Review Netflix’s original culture memo, which some say captures the spirit of their current company more than their current culture memo

FAQs about the Netflix Data Scientist Interview

How should I prepare for a Netflix Data Scientist interview?

You should start by reviewing questions about data science and behavioral interviews. Do as much research as possible on the topic and application of causal inference, including reading books about the subject. You should also read Netflix’s recent culture memo as well as their infamous culture presentation from 2009.

How much do Netflix Data Scientists make?

According to Levels, Netflix data scientists make the following:

• L4: $339,000
• L5: $505,000
• L6: $721,000

How long is the Netflix Data Scientist interview process?

Because these rounds involve a decent amount of coordination with different teams, the process can take around 3–4 weeks.

Can you reapply to Netflix after rejection?

You can reapply for a position at Netflix 6–12 months after receiving a rejection. During this time, it’s a good idea to hone your data science skills, particularly anything you can learn about causal inference techniques.

Does Netflix offer data science internships?

Netflix offers an internship program that typically includes 12 weeks of work with the Data and Insights team. You’ll usually see internship opportunities appear on their jobs page in August.