Three Ways to Close Your Company’s Data Science Skills Gap Now

It should come as no surprise that demand for folks with data science expertise exceeds supply. In fact, according to some McKinsey, there are only half as many qualified data scientists as needed. The good news is the market will likely resolve the shortage in the long run. But in the short run, the talent gap creates some challenges for an organization looking to get ahead with data.

Thanks to their ability to use math and computer science to turn big data into business gold, data scientists are the rock stars of the advanced analytics world (at least as data scientists have traditionally been defined). As more companies start investing in AI, they’ve looked to data scientists to lead the way.

In response to the increased demand for data scientists and other folks at the sharp end of the data science stick – we’re looking at you, data, machine learning, and deep learning engineers —  it’s driven their salaries up into the mid six-figure range. Uber pays its AI engineers an average of nearly $315,000, according to one report, with many other big firms in Silicon Valley and elsewhere paying more than $200,000 per year. Obviously, this is a great time to be a data scientist (or even “research scientist,” as some data scientists have begun calling themselves).

But there’s a big problem with the data scientist supply chain: there’s just not enough of them. While universities have pivoted sharply to data science by adding new degree programs and curriculums, it has barely put a dent in demand. A LinkedIn report from August found more than 151,000 job postings for data scientists, with acute shortages being felt in big tech hubs like San Francisco, New York City, and Los Angeles.

Some compelling statistics about the current data talent situation were laid out in Correlation One‘s “Future of Data Talent” report, which was published today. The report quotes a Harvard Business Review study that found 40% of companies claim they’re “unable to hire or retain data talent due to a lack of supply.”

It also found there will be 2.7 million new data-related job postings in the U.S. by next year (a figure that originated with a PwC study commissioned by the Business-Higher Education Forum). That figure corresponds with an Asia-Pacific Economic Cooperation study that found a 20% increase in demand for data talent by 2020.

“Skilled data professionals are the backbone of any data initiative, yet most companies struggle to identify and hire skilled data talent,” Correlation One’s co-founders and co-CEOs, Sham Mustafa and Rasheed Sabar, write in the report. “Companies need data scientists, data engineers, quantitative researchers, and machine-learning specialists. And they need data analysts, product managers, database administrators, and business intelligence analysts with data literacy.”

One potential way that companies can address the problem is to stop looking exclusively at top-tier universities for data talent, says Correlation One, a company that helps companies find data scientists.

While excellent data science prospects can be recruited out of Ivy League schools like Harvard and Yale and top-tier public schools like NYU and UC Berkeley, with the acute shortage of data scientists and the ridiculously high salaries, companies would do well by themselves to consider graduates from a range of other schools.

This recommendation is born out of empiricism: Correlation One actually tested more than 50,000 students from more than 200 universities to gauge their data science aptitude. The company concluded that excellent data science candidates can be found in places like University of Michigan, University of Illinois, the University of Texas, and UCLA.

“While on average there are more elite data science and analytics students at tier one schools, by volume, there are significantly more elite students at tier two and tier three schools,” the company writes.