You come home from a long day at work, make yourself some dinner, and turn on the TV. Not to MSNBC or ESPN but, like over 100 million global users, you go directly to Netflix. You might watch a Netflix original that attracted you with its clever trailer; or maybe, you dive right in to your ‘Keep Watching’ queue to finish that episode of Friends for the third (or eleventh) time. Maybe it’s date night, so you and your partner browse for a while, searching for a light new Rom Com or, if you’re me and my partner, a hard-hitting documentary. 

What you might not realize is that as you browse, click, pause, rewind, or exit, you’re generating data. A lot of data. To ease your mind, you should know that, unlike your other everyday platforms, Netflix doesn’t share or sell this data. Instead, they use it primarily to make their service better and more user-friendly; for instance, to tailor the user interface specifically for you.

For IU Adjunct Professor in Psychological and Brain Sciences (PBS) Dr. David Landy, Netflix is more than a pastime; it’s also a job! He currently works as a data scientist for Netflix, where he uses mathematical modeling and data scraping techniques to “build a computational notion of what Netflix members might be like and see if it matches what people are actually doing” with the service. 

Landy, whose research more generally relates to how people learn and understand math, came into the job in a non-traditional way. Alongside being an IU professor, Landy helped his partner, who owns a hair salon in Virginia, with financial planning by tracking and analyzing their clients’ behavior: “Who’s coming back or not and why? What were they getting out of the experience? What little changes would make an impact on what clients were experiencing?” Armed with some technical know-how, customized statistical and programming tools, and an intrinsic interest in the problems of client satisfaction, Landy was, for all intents and purposes, a part-time data scientist. 

After coming into contact with an industry-based academic, Dr. Katherine Livins (who has worked for Stich Fix, Netflix, and now Apple), Landy spoke in a session entitled “Building Bridges from (Ivory) Towers: Combining Academia and Industry for Cognitive Research” at the 2017 Cognitive Science Society annual meeting. He subsequently joined the Netflix team at Dr. Livin’s recommendation. As he explains, “I didn’t so much seek out the job as inadvertently discover the opportunity through the people I came across.” 

Networking and exposure, Landry observes, are key to landing a Netflix job. He notes that other schools may feed people into such positions more efficiently even though, at Indiana University, “we train people to have all the skills that they need to really thrive out here.” Many PBS graduates are data scientists in the making. Despite the difficulty of long-distance networking in places like Los Gatos, California, where Netflix is based, “At IU, we are giving people such good, deep mathematical and experimental backgrounds,” he believes. “We’re really training data scientists with the strengths a Netflix job requires.” 

Landy highlights some of the benefits of day-to-day life at Netflix. “Companies out here have done a really good job of re-creating the traditional benefits of universities,” he observes. His schedule is flexible and self-determined, and his access to paid time off and the ability to work from home enables him to keep mentoring his Bloomington-based graduate students from California. The atmosphere, he adds, also promotes innovation and collaboration. “You’re working with incredibly intelligent people,” he explains, “many of whom have Ph.D.’s and a very inquisitive approach,” which is, indeed, much like academia. And, they are running experiments all the time.

Yet, academia, he suggests, may also have something to learn from this innovative corporate environment. He points out, for example, that “the atmosphere at Netflix is more directly mutually supportive; everybody is really trying to make sure that everyone else’s work can get done.” In this world, no one succeeds unless everyone does. 

Which leads to some speculation about how this might translate to a university. So I ask, “Does this mean we should do collaborative dissertations?” (It’s a question that has more than a little relevance to my current circumstances.) “I think we may be better off that way,” he contends. “Before I came out here, I thought it would be a good idea, but now that I’m here, I know it is.”

In this way, each setting provides a unique perspective on the other, highlighting how each workplace is more similar than it first appears. His experience also expands our perspective, showing us where, with a little bit of “networking and exposure,” cognitive science and psychology can lead you.

Edited by Jennifer Sieben and Evan Arnet