Wired explains why there has recently been a huge exodus of Astrophysicists out of Academia and into Silicon Valley where they are highly paid for studying consumer preferences instead of Stellar Dynamics.

To understand what’s driving astrophysicists into consumer product startups, consider the recent explosion of machine learning. Astrophysicists, who wrangle massive amounts of data collected from high-powered telescopes that survey the sky, have long used machine learning models, which “train” computers to perform tasks based on examples. Tell a computer what to recognize in one intergalactic snapshot and it can do the same for 30 million more and start to make predictions. But machine learning can also be used to make predictions about customers, and around 2012, corporations started to staff up with people who knew how to deploy it.

These days, machine learning drives everything from Stitch Fix’s curated boxes of clothes to Netflix’s personalized movie recommendations. How does Spotify perfectly predict the songs that will surprise and delight you in its weekly personalized playlists? That’s machine learning at work. And while machine learning now constitutes its own field of study, because scientists from fields like astrophysicists have been working with those kinds of models for years, they’re natural hires on data science teams.

“We were already in Big Data before Big Data became a thing,” says Sudeep Das, an astrophysicist who now works at Netflix.

Das got his PhD at Princeton, where he researched cosmic microwave background—basically the electromagnetic radiation left over from the Big Bang. Afterward, he spent a few years studying data from the Atacama Cosmology Telescope in Chile. The telescope collects nearly a terabyte of data from the cosmos every night, and from this massive data set, Das detected an elusive astrophysical signal. It was a rare payoff after years of painstaking work. The discovery earned him the attention of the University of Michigan, which offered him an assistant professorship.

But Das turned it down and moved to Silicon Valley instead—first to work as a data scientist at Beats Music, then at OpenTable, and now at Netflix.

The decision to leave academia came down to a few factors: The pay was certainly better, and the jobs were more plentiful. “There’s a bottleneck of getting into tenure-track positions,” he says. And being in the Bay Area meant he and his wife—who is also an astrophysicist—would never have to worry about both finding jobs. But the real surprise, he says, was that the work in tech companies was actually interesting. At Beats, he says, he found “like-minded people who were working on problems that didn’t take away the intellectual high.” Same math, different application.

Das says he’s noticed that more and more physicists are trading the difficult slog of academia—which can involve a decade of financially dicey postdoctoral work—to take cushy jobs in tech. “Only two people from my PhD batch at Princeton are not working in industry,” he says. “You have to be a die-hard academic to stay.”

This big bang extends across the industry. “Astrophysics is our number one domain,” says Eric Colson, Stitch Fix’s chief algorithms officer emeritus. “Most folks have a PhD in a quantitative field, but if we did a histogram, I think astrophysics is number one. They teach math really well—a lot of physicists are better mathematicians than mathematicians. They also teach coding well. They’re better computer scientists than most computer scientists.” …

In academia, astrophysicists can spend years stuck on a singular problem. And many of the exciting problems have already been solved, says Amber Roberts, a former astrophysicist who now helps academics transition to industry at Insight Data Science. “We discovered the size of the universe. We measured the speed of light. We found pole stars. We found black holes,” she says. “A lot of those big things, like understanding how space-time works or how gravity distorts, are what get people interested in the study of space and cosmology. But what you’re really doing is contributing to a very small subfield where you’ll work about three years on a paper that about 10 people in the world are going to read. You’re not going to be Carl Sagan.”

RTWT