Time to Query Your Data – Do You Know Where Your Data Scientist Is
The demand for data scientists has never been higher, as organizations scramble to adjust to the new realities of big data. That high demand presents a challenge for IT and line-of-business managers who are in charge of hiring data scientists. And not only must they lure the rare scientist, they must keep them happy enough to stick around, which may be easier said than done.
The top data scientists today have enormous freedom to pick and choose which jobs they take. No matter where one looks–from Wall Street to Washington D.C., from Silicon Valley to the Ivy League–there is somebody, somewhere, with a big data problem, and a need for an experienced data wizard to solve it.
So what factors differentiate which job a Data Einstein will take? Here’s a hint: obscene salaries and perks like unlimited Doritos and Diet Coke will only get you so far. To successfully recruit and retain a top data scientist, you need to keep his or her brain engaged in a way that occupies their big data pleasure center.
“Few people become data scientists to get rich,” writes James Kobielus, a big data evangelist at Big Blue, in a recent blog post at IBM’s Big Data Hub. “People become data scientists for many reasons, and intellectual stimulation is high on the list.”
According to Kobielus, smart people cluster around leading-edge challenges. “The best way to attract them to your projects is to give them something really exciting to wrap their minds around,” he writes. “If you’re only offering them your most boring projects–in other words, the ones that don’t allow them to stretch their minds and spawn insanely disruptive new solutions–don’t expect them to return your calls.”
In this case, the direct approach–listing a job opening on a general purpose job board–may be the worst way to go about hiring a top data scientist. Recently, a new class of specialized job search engines such as Gild, RemarkableHire, and TalentBin have popped up that can identify top data talent among “passive” job seekers, i.e. those who aren’t even actively looking for a job.
There’s also the crowd-sourcing approach to filling vacancies for data scientist jobs, which may be a good avenue to pursue if finding a data veteran with many years of experience is not the top priority.
“Crowdsourcing data scientist expertise on a moment’s notice is often as easy as engaging the smart people in online communities and, if budgets permit, hiring them for consulting projects,” Koebielus writes. This approach often works for attracting freelance data scientists, who already have a prominent presence on the Internet and are eager to work with their peers and build their resumes.
In the end, building a collegial environment that rewards research, collaboration, and creativity is the best approach for attracting and retaining top data science talent.
According to Koebielus, some of the characteristics that identify such an environment include: the existence of scientific advisory boards that collaborate with academia; data science competitions that pick winners; a rigorous job interview process where applicants must defend their research theses; the availability of advanced training courses; encouraging attendance at professional conferences; letting data scientists pursue their own curiosities; and last but not least, giving data scientists a steady stream of new and challenging projects to work on.