by Angela Guess
Jim Meyerle, co-founder of Evolv recently wrote for The Next Web, “As a co-founder at a big data analytics software company, where recruiting talented data scientists and engineers often feels like finding a needle in a haystack, I regularly get asked about lessons learned in building world class, big data-focused R&D teams. Given that the market for these applications has exploded in popularity, I believe it’s critical that companies in this space have a battle plan for how to recruit, develop and retain exceptional big data R&D talent.“
He continues, “We’ve focused on assembling an R&D team with overlapping capabilities. More than half of our 100-person staff are devoted to R&D and dozens more support our R&D efforts via a ‘leveraged network’ approach. While that includes engineers with requisite skills like Hadoop, the team also includes experts in other disciplines that have parallel (but unexpected) relevance to the work that we do: theoretical physics, artificial intelligence, organizational psychology, econometrics, and others. Here are three practices I recommend.”
The first is: “Recruiting data science talent isn’t just about finding people with technical chops. Often, the more relevant (and overlooked) factor is where and how someone has been able to work with large, disparate datasets to solve broad business problems. To solve ‘thick market’ problems, you want economists (economic consulting firms have troves of great people) to build the best probabilistic risk assessment models.”
Read more here.
photo by:
MiaElliott
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