Netflix Data Scientist Interview: A Complete Guide

Data science roles at Netflix are highly competitive and difficult to land. Fortunately, enough people have successfully gone through the Netflix data scientist interview process to share their experiences and offer valuable advice. Read on to learn more.

data scientist interview netflix

If you’ve graduated from a data science degree or bootcamp, put together an impressive portfolio, polished your programming and quantitative analytics skills, and have scored a coveted interview with a Netflix recruiter or hiring manager, you might understandably be nervous about what comes next.

Interviewing for any role can be an intimidating experience, but the best way to ensure a successful interview is to be prepared for what you’ll be asked and to know what the company is looking for. Data scientists who have interviewed with Netflix share their own experiences below.

What does a data scientist at Netflix do?

When it comes to filling out its ranks, Netflix is known for a few key practices: it almost exclusively hires senior-level professionals who have at least five years of experience, and it hires for ultra-specific roles to meet the needs of its teams. This means the responsibilities of a Netflix data scientist can greatly vary depending on the team they’ve joined—some might spend the bulk of their time working on personalization algorithms, while others might focus on product research and tooling, or teaming up with user interface designers and engineers to figure out how to optimize the user experience.

Regardless of the team, though, Netflix’s data scientists can be sure that their contributions have a meaningful impact on Netflix’s products and services. As a growing number of entertainment companies invest in streaming, the demand for data scientists and analysts has grown alongside it, and nowhere is this more apparent than at Netflix.

“You do not make a $100 million investment these days without an awful lot of analytics,” Dave Hasting, Netflix’s direction of product analytics, said in 2015.

Data scientists at Netflix assist in forecasting, operations research, topic modeling, user segmentation, and content recommendations. They also used data to guide accurate decision-making. For example, the company’s data analytics division has helped the company with planning budgets, finding locations, building sets, and scheduling actors. Data scientists have also built models that allow production executives to make critical decisions using data-centric ‘what-if’ scenarios, rather than relying on their best guesses.

What skills does a data scientist at Netflix require?

Netflix typically hires data scientists to meet the needs of specific teams, which means skill requirements can vary from role to role. That said, most data scientists at Netflix have a quantitative analytics background and sufficient past experience that allows them to take ownership of projects.

Other skills commonly shared by Netflix’s data scientists include proficiency with programming languages such as SQL, Java, Scala, and Python; experience with statistics, hypothesis testing, and developing metrics; experience with engineering data pipelines using big data technologies such as Hive, Hadoop, and Spark; being able to crystalize vague requirements and develop scalable solutions; and strong communication skills.

What is the interview process for a data scientist at Netflix like?

Similar to other technology companies, the data science job interview process at Netflix begins with an online application. Candidates whose résumés impress will then move onto a phone screening with a hiring manager who will ask both HR-type and technical interview questions, before doing onsite interviews at Netflix’s offices in either Los Gatos, CA, or Los Angeles.

  • The phone screener

Data scientists who have interviewed with Netflix report that the initial phone screener can be heavy on technical questions and that the company’s recruiters like to dig deep into a candidate’s prior projects and experience. Expect specific questions about why you chose a certain algorithm for a project, or how you built a certain machine learning or analytics system.

  • The culture dek

Netflix stresses the importance of its company culture, so candidates should be prepared to talk about how they align with that culture. Candidates should also come ready to talk about how prior experiences and projects demonstrate their commitment to Netflix’s values such as independent decision-making, curiosity, clear communication, selflessness, and innovation. Data scientists who have interviewed with Netflix strongly recommend reading about Netflix’s culture ahead of the interview. 

  • The in-person interview

The Netflix interview loop for data scientists consists of interviews with six or seven hiring managers, data scientists, engineers, and executives.

Similar to the phone screener, candidates can expect behavioral/culture questions such as “How do you give feedback? Offer an example,” technical questions such as “What is the best way to communicate ML results to stakeholders?” and product questions specific to Netflix such as “How would you build and test a metric to compare two users’ ranked lists of movie and TV show preferences?”

What happens after the interview process?

While there is no take-home test, past interview candidates have been required to design experiments, answer SQL questions, apply their statistics and probability knowledge to come up with solutions, and discuss regression and classification modeling concepts.

Other questions that former interviewees have been asked include:

  • Given a month’s worth of login data from Netflix such as accountid, deviceid, and metadata concerning payments, how would you detect fraud?
  • What do you know about A/B testing in the context of streaming?
  • How would you select a representative sample of search queries from five million?
  • How would you approach attribution modeling to measure marketing effectiveness?
  • How would you determine if the price of a Netflix subscription is truly the deciding factor for a consumer?
  • What are the differences between L1 and L2 regularization?
  • What is the difference between online and batch gradient descent?
  • Write the equation for building a classifier using Logistic Regression.
  • If Netflix is looking to expand its presence in Asia, what are some factors that you can use to evaluate the size of the Asia market, and what can Netflix do to capture this market?
  • Write SQL queries to find a time difference between two events.
  • How would you design an experiment for a new content recommendation model we’re thinking of rolling out? What metrics would matter?

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