Netflix Research Scientist Interview Questions + Guide in 2024

Netflix is a global powerhouse in the entertainment industry, with over 270 million paid memberships across 190+ countries. The company excels in delivering high-caliber TV series, films, and games in numerous languages.

As a research scientist at Netflix, you will get the opportunity to conduct advanced machine learning research that enhances localization and global entertainment. This position requires a proven research track record in Multimodal LLMs, a relevant MS or PhD, familiarity with ML frameworks like PyTorch or TensorFlow, and strong communication skills. Key responsibilities include researching and productizing Multimodal LLMs, collaborating with ML scientists and engineers, identifying new research opportunities, and presenting findings to various company levels.

In this guide, we will help you navigate the interview process, offering a comprehensive look at the steps involved, sample Netflix Research Scientist interview questions asked, and strategies for success. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following on a Netflix research scientist interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter or hiring manager from the Netflix Talent Acquisition Team will contact you and verify key details such as your experiences and skill level. Behavioral questions may also be part of the screening process.

During this screening round, questions may revolve around Netflix’s culture memo, which you are encouraged to review before the call. This call generally aims to gauge your cultural fit and answer any high-level questions you might have about the role.

The whole recruiter call should take about 30 minutes.

Online Assessment

Candidates who pass the initial screening will be invited to complete an online assessment. This typically includes coding challenges on platforms like CodeSignal, Hackerrank, or similar. Questions may vary from easy to medium difficulty, focusing on data structures, decision trees, and basic machine-learning tasks.

Technical Virtual Interview

After completing the online assessment, candidates undergo a series of technical interviews. These interviews are usually conducted virtually, including video conferences and screen-sharing tools.

The technical interviews typically include:

1. Coding Interview: Questions from medium to hard difficulty levels, focusing on algorithm and data structure problems similar to those found on LeetCode.
2. System Design Interview: Questions related to designing systems and infrastructure at scale.
3. Behavioral Interview: Conducted by a senior manager, this interview focused on Netflix’s culture, past projects, and problem-solving approach.

Each interview round lasts around 45 minutes to 1 hour.

Take-Home Assignment

Candidates progressing past the technical interviews may be given a take-home assignment, typically involving real-world problems related to the job role. This could involve building an algorithm, creating visual promotional media assets, or solving a machine learning challenge.

Panel Interviews

Upon completing the take-home assignment, candidates are invited to a panel of interviews, typically lasting 4 to 6 hours. The panel includes multiple cross-functional leaders who evaluate the candidate’s technical skills, cultural fit, and overall fit within the team.

Questions may vary, including more in-depth coding challenges, case studies, system design problems, and situational questions related to Netflix’s operational challenges.

Decision and Offer

After the panel interviews, the candidate’s performance is evaluated, and a decision is made. Successful candidates are contacted to discuss the offer, which includes compensation details such as a mix of salary and stock options.

Typically, interviews at Netflix vary by role and team, but commonly Research Scientist interviews follow a fairly standardized process across these question topics.

1. How do you calculate the average lifetime value for a SAAS company?

You work for a SAAS company with a product costing $100/month, a 10% monthly churn rate, and an average customer lifespan of 3.5 months. Calculate the formula for the average lifetime value.

2. What metrics/graphs/models would you use to analyze churn behavior for Netflix’s pricing plans?

Netflix has two pricing plans: $15/month or $100/year. An executive wants to analyze the churn behavior of users subscribing to either plan. What metrics, graphs, or models would you build to provide an overarching view of subscription performance?

3. How do you select the best 10,000 customers for a pre-launch and measure its performance?

Amazon Prime Video wants to test a new show on 10,000 customers. How do you select these customers, and what process would you follow to measure the pre-launch performance?

4. How do you analyze cross-platform user experience using web and mobile interaction data?

You need to understand user behavior, preferences, and engagement patterns by analyzing user interaction data on both web and mobile. Write a query to find the percentage of users who visited only mobile, only web, and both.

5. How can Netflix measure the success of acquiring new users through a 30-day free trial?

Netflix offers a 30-day free trial, after which customers are automatically charged. How can you measure the success of acquiring new users through this free trial, and what metrics would you use?

6. Write an SQL query to recommend pages for each user based on their friend’s liked pages.

Create an SQL query to recommend pages for each user based on recommendations from their friend’s liked pages. Ensure that it does not recommend pages that the user already likes.

7. Implement a priority queue using a linked list.

Implement a priority queue using a linked list that supports insert, delete, and peek operations. Smaller priority values imply higher priority.

8. Write a function isMatch(s, p) to implement a simple regex parser.

Create a function isMatch(s, p) to implement a regex parser supporting '.' and '*' operations. The matching should cover the entire input string.

9. Write a function reverse_at_position(head, position) to reverse a linked list from a given position.

Create a function reverse_at_position(head, position) to reverse a linked list starting from the given position to the end of the list.

10. Determine a potential friend for John based on a point-based system.

Using the provided database tables, determine a potential friend for John based on mutual friends, shared page likes, and disqualifications for blocked users and existing friends. Return the top user with the highest friendship points.

11. How would you explain what a p-value is to someone who is not technical?

Explain the concept of a p-value in simple terms to a non-technical person. Focus on its role in hypothesis testing and what it indicates about the results.

12. What are MLE and MAP, and how do they differ?

Define Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP). Explain the differences between these two statistical methods.

13. What is Linear Discriminant Analysis (LDA) in machine learning, and what are its use cases?

Describe the concept of Linear Discriminant Analysis (LDA) in machine learning. Provide examples of practical use cases for LDA.

14. How would you collect and aggregate unstructured data from videos for an ETL pipeline?

You are designing an ETL pipeline for a model that uses videos as input. How would you collect and aggregate data for multimedia information, specifically when it’s unstructured data from videos?

15. Should you separate user groups by the year they became Netflix members when training a boosting algorithm?

You are analyzing a Netflix users dataset collected over 10 years to predict whether a user will trust the website by entering their credit card info for a trial period. When training your model, would it be a good idea to separate the user groups based on the year they became a Netflix member? Why or why not?

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Netflix research scientist interview include:

• Understand Netflix Culture: Netflix values its unique culture heavily. Make sure to review their Culture Memo and be prepared to align your past experiences and values with it.
• Technical Prowess: Netflix interviews involve challenging technical assessments across machine learning, deep learning, and coding platforms. Practice medium to hard questions on platforms like LeetCode and understand machine learning concepts in-depth.
• Communication Skills: Given the cross-functional nature of the role, you should be able to simplify complex concepts and demonstrate excellent communication skills with both technical and non-technical stakeholders.

What is the average salary for a Research Scientist at Netflix?

What is the company culture like at Netflix?

Netflix has a distinctive and innovative company culture that values creativity, collaboration, and risk-taking. It promotes a fast-paced and dynamic work environment and places high importance on diversity and inclusion. Employees are encouraged to be entrepreneurial, take risks, and learn from mistakes.

What benefits does Netflix offer for the Research Scientist position?

Netflix offers a comprehensive benefits package, including health plans, mental health support, a 401(k) retirement plan with employer match, stock options, disability programs, health savings and flexible spending accounts, and family-forming benefits. They also provide paid leave of absence programs and flexible time off for salaried employees.

As Netflix continues to push the boundaries of storytelling and entertainment across the globe, the role of a Research Scientist becomes ever more critical. You can truly stand out and ace the interview by focusing on your technical skills, showcasing your ability to integrate innovative algorithmic solutions, and communicating effectively with cross-functional teams.

If you want more insights about the company, check out our main Netflix Interview Guide, where we have covered many interview questions that could be asked. Additionally, explore our interview guides for other roles such as software engineer and data analyst to learn more about Netflix’s interview process for different positions.

Good luck with your interview!