Big data worker shortage demands job candidates with diverse backgrounds

Companies urgently seek people with IT, math and business backgrounds

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Firms will avoid candidates who have only worked in smaller environments because at "very large enterprise big data programs ... you're talking about huge amounts of data and that could be very overwhelming to someone."

While IT professionals have a grasp of what traits work for data science's technical positions, defining what backgrounds make for a good analyst proves difficult.

These positions go beyond possessing strong technology skills so being a solid developer does not necessarily translate to an analyst job. Companies need employees who can make the data work for the business.

"Companies are really looking for higher level quantitative skill sets for these roles," says Kelley. "It's not every developer you come across. It's someone with that business acumen who can parlay those skills into strategic decision making."

Filling the data analysis roles at BrightEdge entails finding candidates who "understand the right questions to ask around this data and how to tease this into actions that result in business outcomes for our costumers," says Yu. The challenge is finding those who can "artfully bridge the technical capabilities of the cool things you can do and connect the dots to the value. The challenge there is not so much it's new but that's just a harder skill set to find."

DataXu's data analysts need to understand how to boil petabytes of data down into two charts that get to the essential information, says Simmons. "It is an emerging skill that no one learns in college. You have to learn this on the job. We hire smart people and train them."

Playnomics' Burke learned about translating data analysis into tangible business information on an early assignment that involved predicting user longevity on games. His model identified a segment of people that was 85 percent likely to spend significant time playing a game. When executives and marketing staff asked Burke to define that group, his reply of "cluster 32" was not the response they wanted.

"They were looking for us to describe what does the average player look like in that cluster in terms they could understand," he says. "Being a data specialist requires you to not just be somebody who's good at statistics but also good at using those statistics to tell a story."

Burke encourages research scientists whose backgrounds may translate into a data science career to consider the field. Trading academia for the private sector allowed him to continue to solve puzzles with data. Now instead of solving theoretical problems on how disease travels, his models have the real-world effect of helping game companies better understand their business.

"All problems in biology aren't necessarily commercial ones," he says. "The things that we try solve may be purely for academic or knowledge reasons. It's easy to get caught up in those because they're very interesting. But for anyone who is in research science and is inspired by real work applications of that stuff, working in the private sector is an excellent experience."

And the data science jobs will be there for them. Winter, Wyman's Byron has seen an uptick in clients looking for people who can build and maintain large data warehouses.

"The demand outweighs the supply because the demand keeps growing," he says. "It's not like this has been around forever ... so your pool of candidates is smaller."

Related: Colleges incorporate data science into curriculums

Copyright © 2012 IDG Communications, Inc.

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