Netflix is known for being one of the most data-driven companies in the world. But what does that mean for its data scientists? Read on to find out more about the day-to-day responsibilities of a data scientist at Netflix.
Here’s what we’ll cover:
When most people think of big data at Netflix, they think of recommendation algorithms and viewership numbers that allow the company to see which of its shows and movies are most popular. But big data actually goes well beyond those facets of the business, affecting everything from the streaming service’s user interface, to production schedules, to membership acquisition and retention. In fact, data science, analytics, and data engineering are such important parts of Netflix that specialists in those fields work closely with every team in the company to assist in areas such as business strategy, customer insights, data visualization, and analytics at scale.
Current and former Netflix data scientists and analytics specialists share their experiences working at the company below and offer advice on how to succeed at one of the most data-driven entertainment and technology companies in the world.
While Netflix’s reputation for hiring only top performers might make it seem like an intense place to work, many of its data scientists, analysts, and engineers describe it as a collaborative and supportive workplace that gives employees the space to get the job done.
For example, Rocio Ruelas, a senior analytics engineer on Netflix’s content marketing and analytics research team, starts her workday at 8 a.m. by grabbing a complimentary breakfast at the company’s Los Angeles office before jumping into brainstorming sessions with her colleagues. Some days, she works solo to figure out analytics issues or build dashboards.
“One of my current projects involves understanding how viewing habits have evolved over the past several years,” Ruelas said. “We started out with a small working group where we brainstormed the key questions to address, what data we could use to answer said questions, and came up with a work plan for how the analysis might take shape. Then I put on my headphones and got to work, writing SQL and using Tableau to present the data in a useful way.”
Alex Diamond, who also works in data analytics at Netflix, describes a similar day-to-day experience that is split evenly across speaking with stakeholders to understand their needs, brainstorming with team members to plan projects, writing code in SQL or Python, and building visual outputs using Tableau.
“Some days have more of one that than the others,” Diamond said. “The one constant is that my day always starts with a ridiculous amount of coffee. And that it later continues with even more coffee.”
In addition to helping Netflix power its recommendation engines and decide which new shows to greenlight, data scientists and analysts help the company with its business and technical decisions. The company now relies on the work of data scientists for planning budgets, finding filming locations, building sets, and scheduling actors. Data-derived insights help producers determine whether it is more cost-effective to film in one city over another; whether it’s more efficient to hire extras over leaning on visual effects; and whether it’s preferable for a certain project to film over a 10-hour workday versus a 12-hour workday.
Likewise, data-powered automation and mathematical optimization are used to help schedule scenes, talent, and locations for multi-episode shows and data on historical viewing trends assist with localization efforts.
The work of data scientists and analysts can also have a huge impact on the customer experience. For example, Julie Beckley, a data scientist who supports the Encoding Technologies team, whose job it is to improve compression algorithms to efficiently send high-quality video and audio files to customers, uses data science methods to quickly and accurately understand whether customers are having a better experience.
“With these insights, the engineering teams can quickly understand what’s working well and what needs to be improved,” she said. “It’s super exciting to see the impact of my work when I hear from friends and family that Netflix is streaming well for them!”
All data scientists at Netflix are proficient in a range of technical skills such as programming languages (SQL, Python, and R in particular), technologies like Spark, Hadoop, Jupyter notebooks, and Tableau, and have the ability to build and maintain the full data pipeline.
But there are also a number of skills that Netflix highlights as integral to its culture, including the ability to make decisions in the face of ambiguity, a clear and adaptive communication style, a collaborative and selfless approach to work, and an eagerness to understand and learn.
“On a dream team, there are no ‘brilliant jerks’,” Netflix says in its culture document. “Our view is that brilliant people are also capable of decent human interactions, and we insist upon that. When highly capable people work together in a collaborative context, they inspire each other to be more creative, more productive and ultimately more successful as a team than they could be as a collection of individuals.”
In order to succeed as a data scientist—both in general and at Netflix—the company’s data experts have three pieces of advice to share.
If reading about Netflix’s culture deters you from even applying, think again. “When I read the culture doc...it sounded pretty intimidating,” said Rocio Ruelas, the senior analytics engineer. “Phrases like ‘high-performance’ and ‘dream team’ made me imagine an almost gladiator-style workplace. But I 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.”
Netflix might hire for experience, but that doesn’t mean you have to be an expert in everything, according to Alex Diamond, the Netflix data analyst. “Just knowing when to reach out for guidance on something allows you to uplevel your skills in that area over time,” she said. “My weakness is statistics: I can use it when needed but it’s just not a subject that comes naturally to me. I own that about myself and lean on my stats-loving peers when needed.”
“From my experience, being proactive and pushing forward on your ideas is key,” said Julie Beckley, the data scientist who works with Netflix’s Encoding Technologies team. “The values in the Netflix culture document allow for a framework where everyone is a leader to work well—this is because we expect initiative, direct and candid feedback, and transparency in everything we do.”
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