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 had over 150 million paid subscriptions worldwide, including its 60 million US users. With streaming supported 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 in improving 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.
The Data Science Role at Netflix
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:
- Advanced degree (MS or PhD) in Statistics, Econometrics, Computer Science, Physics, or a related quantitative field.
- 5+ years of relevant experience with a proven track record of leveraging massive amounts of data to drive product innovation.
- Experience with distributed analytic processing technologies (Spark, SQL, Pig, Presto, or Hive) and strong programming skills in Python, R, Java, or Scala.
- Experience in building real-world machine learning models with demonstrated impact.
- Deep statistical skills utilized in A/B testing, analyzing observational data, and modeling.
- Experience in creating data products and dashboards in Tableau, R Shiny, or D3.
What are the data science teams at Netflix?
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.
The Interview Process
The data science interview process at Netflix is similar to other big tech companies. The interview 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 of 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 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 the 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.
- What do you know about A/B testing in the context of streaming?
- What are the differences between L1 and L2 regularization, why don’t people use L0.5 regularization for instance?
- What is the difference between online and batch gradient descent?
- What is the best way to communicate ML results to stakeholders?
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The onsite interview is the last stage in the interview process, and it comprises of 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 interview will comprise of 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.
Notes and Tips
- Remember, the goal of the interview is to assess how you can apply analytical concepts and machine learning algorithms and models to predict value in users and content. Brush up on knowledge of statistics and probability, A/B testing and experimental design, and regression and classification modeling concepts.
- Please, please, please remember to read the Netflix culture deck. Culture is everything at Netflix and they have created a unique and famous work culture that they have transcribed into a 100+ page slide deck online.
- At its core, Netflix’s culture is about building a team of high performers and setting them up in an environment that enables them to excel. This is represented by a healthy amount of freedom & responsibility, strong context provided by managers with limited top-down control, and a compensation and promotion system that rewards A-players.
- In offer negotiation, note that the compensation packages at Netflix are extremely high. Their average salaries for technical hires exceed $300,000 and many times is almost always in cash with an option to convert some into RSUs. This is why their interviews are difficult, and baseline to hire is super high.
Netflix Data Science Interview Questions
- Write the equation for building a classifier using Logistic Regression.
- Given a month’s worth of login data from Netflix such as account_id, device_id, and metadata concerning payments, how would you detect payment fraud?
- How would you design an experiment for a new content recommendation model we’re thinking of rolling out? What metrics would matter?
- Write SQL queries to find a time difference between two events.
- How would you build and test a metric to compare two users’s ranked lists of movie/tv show preferences?
- How would you select a representative sample of search queries from five million?
- Why is Rectified Linear Unit a good activation function?
- 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?
- How would we approach to 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?
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