Data science is still white hot, but nothing lasts forever

Quint Gribbin (cq), 25, is a Data Scientist for Red Owl Analytics. He's held several jobs and moved around the country several times over the last couple of years, and as such, is emblematic of the current generation of adults in this economy.
BALTIMORE, MD -- AUGUST 20: Quint Gribbin (cq), 25, is a Data Scientist for Red Owl Analytics. He's held several jobs and moved around the country several times over the last couple of years, and as such, is emblematic of the current generation of adults in this economy. . (photo by Andre Chung for The Washington Post via Getty Images)
Photograph by Andre Chung — The Washington Post/Getty Images

Every day tech industry execs bemoan the lack of data scientists—the people who theoretically know how to look at the data your company generates, and delve into it to derive the all-important insights we keep hearing about.

And on Thursday, the hype continued. At a Big Data panel hosted by Silicon Valley Bank and Hack/reduce in Boston, nearly 100% percent of the speakers talked about the dearth of qualified data scientists and the impact that is having on business. Several actually said the time to hire a data scientist was: “yesterday.”

What do you expect? Two years ago, The Harvard Business Review, apparently an arbiter of such things, dubbed data scientist “the sexiest job of the 21st century.”

So, if you can’t go back in time to hire a data scientist, sooner is still better than later, said Michael Schmidt, CEO of Nutonian, a Cambridge, Mass. data analytics startup. It’s good to have the data person in place before you start mapping out plans for what you’re going to do with the data and whether and how to clean it up.

But hiring a data scientist presupposes that there is a data scientist out there to hire and therein lies a problem. According to Dr. Tara Sinclair,’s chief economist, the number of job postings for data scientist grew 57% for the first quarter this year compared to the year-ago quarter. And searches for data scientist grew 73.5% for the same period.

One problem with job post data is that “data scientist” is a loosey-goosey term. Generally speaking, practitioners are expected to know statistical analysis, predictive modeling and programming. Oh, and having a certain artistic flair to guide how results are visualized is a definite plus. But ask a dozen hiring managers and you may get a dozen variations on that theme.

So there’s that.

And then there’s the money. The top-paying job listings at Facebook (FB) and LinkedIn (LNKD) are for data scientists—not software engineers, said Sirish Raghuram, CEO of Platform9, a Sunnyvale, Calif. cloud startup. This is a very lucrative field.

One way for prospective employers to guard against sticker shock is to not advertise for a data scientist at all, said Alex Cosmas, chief scientist at Booz Allen Hamilton. “The very term pushes their value up. Advertise for analysts and train them as data scientists.”

Another note: While technical expertise is important, there may be more important attributes. “We look for raw inquisitiveness, the intellectual curiosity which will repay you ten fold. They’ll be so annoying about the data sets they want and the introductions they need that they’ll drive you crazy,” he said.

In counterpoint to all this are those who say that the data scientist shortage has been overblown.

At the MIT CIO Symposium Wednesday, ADP (ADP) VP and chief security officer Roland Cloutier, advised caution for people looking to jump into data science. Yes, he said, young people should learn to program But, they should avoid getting into data science despite the current high demand because that demand will be short lived. “Over time, software will do more and more of what data scientists do today.”

Existing tools like Tableau have already sweated much of the complexity out of the once-very-hard task of data visualization, said Raghuram. And there are more higher-level tools on the way from a cadre of second-generation data science companies that will improve workflow and automate how data interpretations are presented. “That’s the sort of automation that eliminates the need for data scientists to a large degree,” he said.

And as the technology solves more of these problems, there will also be a lot more human job candidates from the 100 graduate programs worldwide dedicated to churning out data scientists, said Peter Kuper, partner with In-Q-Tel, a VC firm affiliated with U.S. security agencies.

Supply, meet demand. And bye-bye perks.

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