At a time when their skills are needed in organizations of all shapes, sizes, and industries, data scientists are in short supply, representing one of the most significant skills gaps in today’s labor market. But what if recruiters are coming up short not because there aren’t enough qualified candidates, but rather because their definition of “qualified” is too constrained? Vin Vashishta, founder and chief data scientist at V-Squared Data Strategy Consulting, makes the case at Fast Company that employers are chasing unrealistic qualifications for their data talent:
I honestly feel for recruiters who are tasked with filing data-science and machine-learning job openings. The list of requirements that employers draw up for those roles is pure bravado with a side of madness: “10 years of data science with at least five years in natural-language processing and either a Master’s or PhD” (never mind that I can count on one hand the number of data scientists who were building for production back in 2007). Others ask for experience with three different programming languages, 10 platforms, a niche algorithm set, leadership skills—and by this point I’m typically only halfway through reading the job qualifications.
Ask any tech recruiter and they’ll tell you about the stack of job openings like these that they’ve been unable to fill for the past six months to a year. Every couple of weeks, the client calls and berates them for not being able to send them quality candidates. After awhile everyone involved throws up their hands and calls it a “skills gap.” It isn’t.
Google doesn’t require a PhD to be a machine-learning engineer. A recent survey found that only one in four data scientists has a PhD. Yet I still see advanced-degree requirements on the vast majority of data-science and machine-learning job descriptions. Most companies just throw it in unthinkingly. But unless they’re investing heavily in advanced research, it’s pointless.
Over-reliance on educational qualifications and experience for emerging roles is a something employers will have to get over if they want to fill talent shortages in data science and other valuable technical roles. Many organizations seek out computer science majors to fill these roles, but many talented computer programmers and software developers didn’t study computer science in college, or don’t have traditional college educations at all.
One company that has taken an innovative approach to filling the data science skills gap is Airbnb, which developed an internal “data university” to teach employees data science, from the basics of data-driven decision making to advanced courses on machine learning and programming languages like Python.
This rethinking of qualifications for high-tech jobs represents the emergence of a new type of employee, which IBM CEO Ginni Rometty has called the “new-collar” worker: Not college-educated, but trained in valuable technical skills through bootcamps, vocational education, or on-the-job training. In a feature at the New York Times, Steve Lohr takes a look at how these middle-skill or new-collar jobs are creating a new path to prosperity for Americans without degrees:
The skills-based concept is gaining momentum, with nonprofit organizations, schools, state governments and companies, typically in partnerships, beginning to roll out such efforts. On Wednesday, the approach received a strong corporate endorsement from Microsoft, which announced a grant of more than $25 million to help Skillful, a program to foster skills-oriented hiring, training and education. The initiative, led by the Markle Foundation, began last year in Colorado, and Microsoft’s grant will be used to expand it there and move it into other states. …
It is unclear whether a relative handful of skills-centered initiatives can train large numbers of people and alter hiring practices broadly. But the skills-based approach has already yielded some early and encouraging results in the technology industry, which may provide a model for other industries. These jobs have taken off in tech for two main reasons. For one, computing skills tend to be well defined. Writing code, for example, is a specific task, and success or failure can be tested and measured. At the same time, the demand for tech skills is surging.