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"Caitlin Lewis Smallwood ’88 is vice president
of science and algorithms at the world’s leading “internet television
network.” From her office in Los Gatos, Calif., she oversees the numbers
that help Netflix learn what you like to watch, predict what you might
want to watch next, and decide what to purchase and produce in the
future." says
She can also see cranes, dump trucks and construction workers building Netflix’s brand-new corporate headquarters on the south end of Silicon Valley. She’s a leader in a growing field within an influential company during a historic moment: data science is helping Netflix take off.
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| Number cruncher: Caitlin Lewis Smallwood ’88 directs a team of around 50 data scientists, engineers and mathematicians at Netflix in Los Gatos, California. Photo by Timothy Archibald |
She can also see cranes, dump trucks and construction workers building Netflix’s brand-new corporate headquarters on the south end of Silicon Valley. She’s a leader in a growing field within an influential company during a historic moment: data science is helping Netflix take off.
“It’s incredibly inspiring to me to be involved in this company when we’re at a point where we’ve launched mostly globally,” Smallwood says. “It’s an amazing opportunity to really help cultures learn about one another in an innocuous, non-threatening fashion.”
Taste clusters
There is a dizzying number of conference rooms
at Netflix headquarters, and they’re not even finished building it yet.
Each one is a little different and named after a famous movie (usually
one that is available to stream via the service). Many feature a glass
wall, frosted with the image of a notable scene or actor.
And then there are the Emmys.
The lobby of Smallwood’s building is full of movie- and TV-based touches: art books about film on reclaimed wood tables, a constant stream of Netflix products on a giant screen (in this case, “The Crown”), and two softly lit columns displaying Netflix’s Emmy awards. Emmys aren’t common in Silicon Valley, but they’re testaments to the company’s smarts and strategy.
Then there’s the data.
When a user logs into the Netflix service and begins browsing, they see rows of categories. While you browse, Netflix logs what you watch, how you found it, how long you watched it, and the device you watched it on, among other things. As Netflix learns more about your viewing habits, it gets better at predicting what you like. This works no matter where you are in the world.
“One thing we’ve learned that holds true — so far, anyhow — is that when you try to get an understanding of people’s tastes, the kinds of ‘clusters of taste’ that people have are pretty similar around the world,” says Smallwood. “The size of the audience for these different kinds of tastes can differ quite a bit from region to region, but the actual kernels of what those tastes are, are not dramatically different.”
On Netflix, tastes are displayed as rows of categorized content. Usually, the rows are the typical fare from the old Blockbuster shelves: drama, comedy, action, sci-fi, romance and so on. But as you move further into Netflix’s database of over 50,000 row titles, things get really specific — “Strong Female Lead,” “Raunchy Cult Late-Night Comedies,” “Quirky Romances,” “Supernatural Horror Movies” and so on. There are a number of websites dedicated to chronicling the most obscure categories delivered to Netflix subscribers all over the world — like “Gritty British Prison Movies.”
“Although our internal job is harder,” she
says, “the output to our customers is actually a little bit better
because you can discover nuanced pockets of taste because of other
regions that then help you serve members in a different region even more
effectively. That part’s exciting, too.”
Smallwood is in charge of more than 50 engineers, data scientists and mathematicians who are working to distill the viewing habits of over 86 million Netflix subscribers and make the product better. Algorithms orchestrate the viewer experience, and they’ve responded: to the tune of 125 million hours watched per day. That’s like watching “Star Trek IV: The Voyage Home” more than a million times daily, or that one episode of “Murder, She Wrote” nearly 2.7 million times. It’s a colossal amount of data, and it’s up to Smallwood to make sense of it.
Smallwood is in charge of more than 50 engineers, data scientists and mathematicians who are working to distill the viewing habits of over 86 million Netflix subscribers and make the product better. Algorithms orchestrate the viewer experience, and they’ve responded: to the tune of 125 million hours watched per day. That’s like watching “Star Trek IV: The Voyage Home” more than a million times daily, or that one episode of “Murder, She Wrote” nearly 2.7 million times. It’s a colossal amount of data, and it’s up to Smallwood to make sense of it.
