Jim Meyerle is the co-founder of Evolv, a big data company that optimizes performance of the work force.
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As co-founder of a company wide data analysis software, where recruitment of scientists and engineers talent Data often feels like finding a needle in a haystack, I regularly get asked about lessons learned in building world-class, big data R-oriented teams and D.
Since the market for these applications has exploded in popularity, I believe it is essential that companies of this area have a battle plan for how to recruit, develop and retain outstanding capital R and talent data D.
We focused on assembling a R & D team with capabilities that overlap. Over half of our staff of 100 people dedicated to R & D and dozens others support our R & D efforts through a "leveraged network" approach.
Although it includes engineers with skills such as Hadoop, the team also includes experts in other disciplines that parallel (but unexpected) interest in the work we do: Theoretical Physics , artificial intelligence, organizational psychology, econometrics, and others.
Here are three practices that I recommend:
1. Recruit talented science data is not only to find people with technical chops
Often, the most relevant factor (and neglected) is where and how someone was able to work with large sets of disparate data to solve broad business problems. To solve the problems of the "market" thick, you want economists (economic consulting firms have large troves people) to build the best probabilistic risk assessment models.
You will do great by hiring actuaries code on the side, and most of the great minds of artificial intelligence built their foundation of basic knowledge in the study of theoretical physics. Many of these disciplines are more relevant data that a researcher makes on a daily basis that controlling MapReduce libraries.
By identifying the industries and businesses that have people with these types of environments, you will have a major advantage.
2. A lot of talented toughest recruiting data is actually free
No, I'm not kidding. The best and brightest minds in the great science data usually end in a research role in a large academic research institution at some point in their lives.
With little to no job experience, Larry Page and Sergey Brin have certainly smart enough during their PhD program to solve one of the most complex data problems of the world; Alex Karp (founder / CEO of Palantir) has a PhD in social neoclassical theory; and Joe Hellerstein (Founder / CEO TriFacta) was a teacher himself for a number of years before starting his own business.
This is surprisingly unknown is that the Masters or PhD candidates often struggle to get their hands on the real world, existing data sets, and jump at the chance to transform these data into information as long they can use these findings to further their own research practice.
In Evolv, we have structured research partnerships with eight major US universities (University of Pennsylvania and the Northwest of them), where control and doctoral students (as well as permanent teachers) work with our data to transmit their research opportunities, while they also work with us in the IP incredible development.
Working with a number of these talented minds gives us a "first look" at some of the hottest candidates to hit the private labor market -. Even before they officially announced their candidacy
3. harder to recruit the best minds in the field of big data is accessible if the mission of your business is related to their person and personal interests
It is shocking how little founders spend in actually learning what the true interests are the people they seek to recruit. Go to GitHub to understand their engineering style, pay attention to their thesis. Many of our brightest in our group technical R & D we have joined spirits because our mission aligned with the personal interests they openly advocate.
Our chief analytics officer (PhD in Econometrics from Wharton) had always wanted to apply the methods of biostatistics in human capital data (it does now); our chief architect (PhD in Theoretical Physics, MS in computer science) said learning machine could more quickly advance the results of matching employee employer but was not always able to find the good place to test his theory.
Although there are many other lessons learned in building great teams R & D data, the above three are the most important. They are ultimately the principles that have allowed us to develop large scale data products offer tremendous value for our customers and employees, and the empowerment of winning business models.