The deluge of data flowing into many companies from the Web, social networks, mobile devices and other sources is driving an urgent need for companies to find and hire people who can collect and interpret that data.
For those with the technical chops to muzzle big data and the business acumen to mold it into actionable insight for businesses, there are five cities driving big data job growth, according to Modis, an IT staffing firm.
San Francisco tops the list, followed by McLean, Virginia, Boston, St. Louis and Toronto. Modis notes that the top jobs in these cities include data scientists, data analysts, business intelligence professional and data modelers.
Laura Kelley, a Modis vice president in Houston, notes that these roles have become more important to companies as data volumes have grown. However, she adds that the right candidates can be particularly difficult to find because “many roles require a complicated blend of business, analytic, statistical and computer skills – which is not something a candidate acquires overnight.”
Companies are also looking for people who have worked in big data environments before, and there are not a lot of people with this type of experience on their resumes, she adds.
Companies hiring data scientists include heavyweights like Google, StumbleUpon, PayPal and Facebook. Research by MGI and McKinsey’s Business Technology Office predicts a shortage of the skilled analysts and managers needed for companies to fully exploit big data.
The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to make decisions based on that analysis, the research finds.
That’s why universities across the country have been refining their offerings to develop the data scientists who are in such high demand. Michael Rappa, director of the Institute for Advanced Analytics at North Carolina State University, says annual enrollment in his graduate program has jumped from 40 to 80.
He notes that while MBAs understand business concepts like product development and management, they often can’t analyze and interpret data. On the flip side, mathematicians and statisticians don’t have the necessary strategic insight into the business.
“Data scientists must have openness to solving business problems, not just be able to perform some nifty modeling. We educate students in a way that cuts across disciplines,” Rappa says.
Rappa notes that 100% of the students in the program have jobs before graduation. One of the companies seeking out the students with the skills that Rappa describes is PayPal, where Chief Scientist Mok Oh is creating a “fantasy data science team” that he’s hoping to bring to fruition with people who have the skills necessary to predict buying patterns from petabytes of data.
Oh says 80% of the team will be made up of PhDs who are focused on machine learning, natural language processing and data mining, while 10% will be statisticians to develop key performance metrics, and 10% will be MBAs who know the right business questions to ask.
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