Netflix is one of the top entertainment and technology companies for data science specialists, offering its employees the opportunity to work on everything from personalization algorithms to film and television production planning. Read on to learn more about the Netflix data scientist hiring process.
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Netflix offers some of the most exciting work opportunities for data scientists. Formerly a DVD rental service, it has grown to become one of the biggest players in the entertainment industry thanks in no small part to its use of big data to inform its streaming business, the movies and TV shows it produces, and the experiences it creates for its millions of global subscribers. In fact, Netflix boasts that it integrates data science across all its different teams, which ensures that the company is putting the massive amounts of data generated to use as it develops key business metrics, tries to understand user behavior, and builds new content recommendation models.
Getting a foot in the door at Netflix isn’t easy, though. The company is known for strictly hiring data scientists with years of relevant experience who can prove themselves to be indispensable to the business.
Read one to find out how to get hired as a data scientist at Netflix—and don’t forget to check out the guides below!
Similar to many big tech companies, Netflix’s culture is shaped by a widely publicized list of values that include independent decision-making, clear and candid general communication skills, curiosity, and results over process.
“Our version of the great workplace is not great gyms, fancy coffees, or frequent parties,” Netflix says. “Our version of the great workplace is a dream team in pursuit of ambitious common goals, for which we spend heavily. It is on such a team that you learn the most, perform your best work, improve the fastest, and have the most fun.”
While this may sound intimidating, current data specialists working at Netflix say that the company’s values result in an encouraging and supportive work environment.
“When I read the culture doc...it sounded pretty intimidating,” said Rocio Ruelas, a senior analytics engineer. “Phrases like ‘high-performance’ and ‘dream team’ made me imagine an almost gladiator-style workplace. ButI quickly learned this wasn’t the case… everyone just wants to do their best work and help you do your best work, too. Think more The Great British Baking Show and less Hell’s Kitchen.’ Selflessness really is embraced as an important Netflix value.”
In addition to paying some of the highest salaries for data scientists in entertainment and tech, Netflix offers the following benefits to its employees:
The base salary for a Netflix data scientist depends on years of experience, education, and location, and the total compensation can vary greatly depending on whether an employee opts to be paid primarily in cash or stock.
Netflix doesn’t have a formal internship program, nor does it typically hire entry-level technical staff. When it does accept interns, they are typically brought on as contractors who are paid around $56-61/hour, according to Glassdoor.
Nearly all data scientists at Netflix have at least five years of experience and are given the title “senior data scientist”. Many hold an advanced degree in computer science, statistics, mathematics, econometrics, physics, or related fields. Average salaries range from around $200,000-$400,000.
Netflix’s interview process for data scientists typically includes an initial phone screen with recruiters where they ask HR-style questions about an applicant’s background and past experience. This is followed by a technical screen where applicants can expect to show their understanding and fluency of programming languages such as Python, Java, Scala, and SQL; data products such as Spark, Hadoop, Tableau, VC Code, and Hive; and deep statistical skills such as statistical modeling, A/B testing, and using observational data and attribution models.
If invited to Los Gatos of Los Angeles for an onsite interview, applicants can expect the data science interview process to include meetings with at least five or six data scientists, engineers, product managers, and executives. Questions might cover the kinds of lessons an applicant has learned from past projects and how they would approach Netflix-specific issues such as, “How would you determine if the price of a Netflix subscription is truly the deciding factor for a consumer?” and “How would you design an experiment for a new content recommendation model we’re thinking of rolling out?”
Compared to technical questions asked during the earlier hiring manager interview, the in-person technical interview will be a deeper dive into the technical aspects of the job, with data science interview questions such as: “What are the differences between L1 and L2 regularization?” and “Write the equation for building a classifier using Logistic Regression.”
Netflix is unique among major tech companies in not offering a formal internship program. Instead of building a technical workforce pipeline of newcomers, the company instead hires data scientists with a proven track record who don’t need additional training.
When the company does accept interns, they are brought on as contractors and expected to hit the ground running by building dashboards, scalable data pipelines, and systems that can make recommendations. They are also expected to apply their knowledge of statistics to business analytics.
Those who are offered internships typically have a master’s degree or Ph.D. in computer science, mathematics, statistics, or a related quantitative field; have strong programming skills; and excel at working independently.
A Netflix data scientist’s day-to-day is largely determined by the product teams they’re on. While all of Netflix’s data scientists are skilled in programming languages, data and hypothesis testing, data exploration, experiment design, artificial intelligence, collaboration with software engineers and product managers, and communicating findings to stakeholders, each data scientist is given the opportunity to specialize depending on the needs of their team.
For example, data scientists working to improve Netflix’s user interface might work closely with an engineering team to tease out actionable insights from user behavior data. Meanwhile, data scientists working on Netflix’s studio projects might use data-derived insights to help with planning budgets, finding filming locations, building sets, and scheduling actors.
Netflix is known for hiring data scientists who are at the top of their game—this means possessing a strong technical foundation, having real-world experience with data science projects, and being a confident creative problem-solver.
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