Hot, sexy and in demand

More than seven years ago, McKinsey & Company famously predicted that by 2018 there would be a shortage of 140,000-190,000 people with “deep analytical skills” (i.e., data scientists) in the United States. A year later, a 2012 article in the Harvard Business Review just as famously labeled data science “the sexiest job of the 21st century.”

Now that 2018 is here, how do those statements hold up?

Well, two months ago, an article by Michael Sasso of Bloomberg News declared data science “America’s hottest job.”

So, data science is hot, sexy and in great demand. What can possibly go wrong?

The cover of the June 2018 issue of OR/MS Today, the membership magazine of INFORMS, offers a hint: “Is ‘fake’ data science a threat?” Written by longtime INFORMS member Doug Samuelson, the cover story denounces “charlatans” claiming to be data scientists and doing high-end analytics such as operations research without serious bona fides in either math or statistics. As I wrote in this month’s Analytics newsletter, at a time when the supply of qualified data scientists can’t keep up with demand, it’s no surprise that some erstwhile “business consultants” are hanging out the “data science” shingle a little loosely.

To that point, Bloomberg’s Sasso quoted Daniel Gutierrez, managing editor of insideBIGDATA, who said, “A lot of people are transitioning from other fields like economics, psychology, mathematics because they see the [data science] field is exploding and there’s money to be made.”

Which begs the question: How, exactly, does one “transition” from psychology to real data science without an extensive background in – and a deep understanding of – mathematics and statistics? An undergrad math or stat class won’t get you there. Not even close.

In his article “Bridging the data science gap” in this issue of Analytics, Seth DeLand of MathWorks offers another option on how companies might meet their need for data scientists: leverage the value of in-house engineers by tapping their domain expertise. After all, he says, “these highly skilled employees have spent countless hours designing the tools their organizations rely on every day, and their familiarity with the DNA of the business makes them suitable to develop data science programs that fit the unique anatomy of their organization.”

OK, but does that make them real “data scientists”? Not until they really earn it.