Big Data and Analytics are all the rage. And, as we all depend upon analytics to drive business and stay competitive hiring, developing, and retaining analytical and big data talent is becoming more critical. Over the past year, much of this conversation has spotlighted on the data scientist, that sexy elusive fantastical beast of legend. But, we’ve spent so much time trying to figure out who the data scientist is, that we haven’t yet asked the next logical question: Once we find him or her, how to we keep him or her?
At an event earlier this year, I had the opportunity to participate in a CIO Think Tank with a few dozen leading data minds from Bank of America, Boeing, Neilsen, Sony, and more. In our informal roundtable discussion, we focused first on identifying and hiring the right talent, the often-different types of development they prefer, and finally how to maximize the potential to retain the best talent.
However, even with some of the best and brightest all gathered around one table, there was still a high degree of ambiguity over the skillset needed of the data scientist, concern for why math-based PhDs would want go into industry rather than academics, and then -- after navigating an environment of limited supply and limited affordability to capture the data scientist -- how do you keep them happy?
All of these made for interesting questions with no readily available answers. So, I asked.
Skillset vs. Mindset
Some say the data scientist is a PhD statistician or mathematician with business acumen and a knack for communicating technical info. Others believe it’s an MBA grad with tech savvy. And, still some say it’s both or neither, or that the data scientist is a myth, or that it’s really the everyday analyst that holds the real value. Regardless of what works for your particular organization or job description, the key is this: Hire for capability, not for skill set. The skill set will change, the mindset, however, won’t.
“I had to divorce the idea that education is some type of job training, or that I was learning skills that would be directly applicable later on,” reflects Dr. Nathan Halko, resident data scientist for Boulder-based SpotRight. “I was very green coming out of school; I learned from scratch most of what I do now. And, I expect when I move on from this position I will have to re-learn a whole new set of problem specific skills. I hope so at least, it’s what keeps things interesting.”
Therein is the rub: it isn't just about the knowledge the data scientist brings with them. It’s about the willingness the mindset to keep learning.
Do PhDs Want to Work or Teach?
Certainly, the data scientist is no stranger to this question. Yet, just as soon as someone asks the inevitable, “What are you going to do with a doctorate degree?” they almost immediately answer their own question with another, “Teach?” Indeed, academics are a very competitive sport and top positions are relatively few. However, today’s data-driven companies can't afford to be without people thinking and solving problems at the intersection of mathematics, data analysis, and computer science.
“I know of a handful of people in my program [at University of Colorado] that are now data scientists, researchers, and machine learning experts in industry,” says Halko. “I don't know what the industry scene was like 10 years ago as I was blissfully studying math for the fun of it. I didn't know that the data scientist would emerge as such a key role in industry/business, but from what I gather that has been changing only recently.”
Yes, there are many opportunities to work on interesting problems outside of academia, and the data scientist is willing to tackle them, too. (More important, at least according to Halko, is that math is fun?)
The Secret to Keeping Them Happy
So, then, what’s the secret sauce to keeping the data scientist? “It’s all about finding the right mix of flexibility, interesting cutting edge projects, self direction, and rewards that will meet the needs of your top people,” says Dave Angulo, cofounder and CTO of SpotRight. “That is what sells them on joining you and that is what keeps them happy working for you.”
Remember: The key to discovery is curiosity. Not just in curiosity-enabling technologies, but more important, in curious personalities. This is, ultimately, the demystification of the data scientist it’s not limited to a dissection of their skills, academic prowess, or depth of business knowledge, but in the personality type that is driven by curiosity. The discovery culture in the business as it relates to the people, then, is a function of matching personality to tools.
Lindy Ryan is research director, data discovery and visualization, Radiant Advisors.
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