Day-in-the-Life of a Data Scientist at Facebook
Sakshi Gupta | 5 minute read | July 8, 2020 
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With billions of users across a suite of social media-defining apps and platforms, Facebook is a data-collecting and data-generating juggernaut. It’s no surprise, then, that data scientists—who help the company make sense of its data, tease out actionable insights, and, ultimately, drive the direction of the business—play such a significant role at the company.
The work of Facebook’s data scientists helps determine what products are developed, how services are improved, and what new opportunities the company pursues. It may seem like an overwhelming and high-pressure responsibility, but Facebook spreads the work across thousands of data and research specialists who are each given the opportunity to home in on a specific part of the business to ensure that they can make the greatest impact.
Springboard alumnus Jaime Torres was a Ph.D. chemist before he retrained as a data scientist and began working in business intelligence at Facebook. Below, he shares his experience and how, despite being one of many data scientists at Facebook, his individual contributions continue to make a difference at one of the largest tech firms in the world.
Facebook 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 Facebook.
What does a day in the life of a data scientist at Facebook look like?
While the long hours and workplace sleepovers of Facebook’s early days are a thing of legend, Torres told Springboard that everyone at the company has a healthy attitude toward work and is given plenty of flexibility. Torres starts his workday at 8am and wraps by 4:30pm. (He swears this is true.)
“You get to work with a lot of smart people, very personable people,” Torres said. “Everyone’s just driven to get their work done, and you can see that people are very excited about what they do. If you get your work done in a reasonable amount of time and manage expectations, you should be fine—I don’t find myself working 14-hour days.”
A typical day for Torres consists of attending planning meetings and before diving into data sets to find answers. In planning meetings, Torres said that cross-functional teams that include data engineers, marketers, and programmers get together to scope out the work of a particular project or campaign, determine the key metrics of success, and agree upon a timeline. From there, everyone gets down to business.
“I spend my days looking for data, building pipelines, and putting them into production,” Torres said.
Responsibilities and impact of data scientists at Facebook
Torres is part of a team that uses data to help Facebook encourage small businesses across the globe to use its family of platforms and tools. For example, where many small businesses in Southeast Asia currently use WhatsApp as a way of communicating with customers, small businesses in the U.S. and Australia tend to rely more heavily on Facebook and Instagram. Torres’ team helps identify those who would not only benefit most from Facebook’s tools (such as traditional brick and mortar stores that need to transition to e-commerce during Covid-19) but are likely to be advocates for it, too.
In his role, Torres created logistics models using Facebook’s existing data sets to make the sales funnel more efficient.
“Part of the model I built lets us identify those users that are more likely to advocate for Facebook because it actually works for them,” Torres said. “There are features in the model that we can select that tell us whether someone will be a good advocate. It takes a giant sales funnel and makes it more efficient.”
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Important skills all data scientists at Facebook need to master
The main skills he uses to pull off these kinds of projects include foundational data science and strong communication. In the former, Torres said that he spends the bulk of his time building data pipelines, cleaning up data, and forming hypotheses around the problems he’s trying to solve—basic data science skills learned through Springboard’s bootcamp that have allowed him to ask nuanced questions and solve complex problems. In the latter, Torres stressed that no data scientist works alone, and if you can’t clearly communicate your findings or manage expectations, then the work can easily become lost or misunderstood.
“At the end of the day, having strong communication skills might be the most important thing,” Torres said. “Not only do you have to wrap up the project and explain the results to your manager and stakeholders, you also have to manage expectations. So, constant communication between yourself and cross-functional teams is very important.”
2 tips for succeeding as a data scientist at Facebook
In order to succeed as a data scientist—both in general and at Facebook—Torres has two pieces of advice to share:
- Focus on the fundamentals of data science
“When you start doing the Springboard curriculum, you go through ‘What is data?’, Python, data structures, and they’re not exactly the sexiest things in the world,” Torres said. “Everyone is probably in a rush to jump into hot topics like AI and whatnot. But, in actuality, I do a lot of SQL querying and cleaning up data—all the foundational stuff. So, it’s very important to get that foundation down.”
According to Torres, these foundational data science skills are transferable and can be built upon, and they’re not limited to the technical aspects of data science, either. Torres noted that being able to ask insightful questions and problem solve—the critical and creative thinking aspects of data science— can drive impact as much as being a strong coder.
- Never stop learning
Data science is a rapidly evolving field—programming languages go out of style, industry best practices are updated and replaced—and any data scientist wanting to remain relevant should continuously invest in upskilling, Torres said.
“I enjoyed doing the Springboard course because I was learning new things and acquiring new skills that I could immediately apply,” he said. “Even if you’re a seasoned data scientist…there’s something new you can learn. If you want to be relevant from today into the future, never stop learning.”
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