"The people who do the best are those that have an intense curiosity," says Patil, whom Forbes magazine credited, along with Cloudera founder Jeff Hammerbacher, with inventing the term data scientist. Previously Patil worked at LinkedIn -- his titles included head of data products, chief scientist and chief security officer -- helping develop that company's data science team and strategy.
Patil has a Ph.D. in applied mathematics. Sacheti has a Ph.D. in agricultural and resource economics. And yet, the qualities of curiosity and creativity matter more than the level and type of academic credential, Patil says. "These are people who fit at the intersection of multiple domains," he says. "They have to take ideas from one field and apply them to another field, and they have to be comfortable with ambiguity."
Cloudera's Wills, for example, took a circuitous path to become a data scientist. After graduating from Duke University with a bachelor's degree in math, he pursued a graduate degree in operations research at the University of Texas on and off, while working for a series of companies, dropping out to take a job at Google in 2007. (He did eventually complete that master's degree, he points out.) Wills worked at Google as a statistician and then as a software engineer before moving to Cloudera and assuming his data science title.
In short, big data folks seem to be jacks of all trades and masters of none, Wills says. "You can take someone who maybe is not the world's greatest software engineer, [nor] the world's greatest statistician -- but they have the communications skills to talk to people on both sides" as well as to the marketing team and the C-level executives. Their biggest skill is in serving as the "glue" in an organization, and most organizations have them, he says.
"These are people who cut across IT, software development, app development and analytics." Wills thinks such people are rising in prominence at companies. "I'm seeing a shift in value that companies are assigning to these people."
Sacheti, too, keeps his eye out for such people internally. "We are finding there are a lot more who are flexible in learning new skills, willing to do iterative design and agile thinking," he says.
In an attempt to hone in on the career paths of big data professionals, IIA and Talent Analytics recently completed an online poll that aims to quantify not only the skills and academic degrees of current data professionals, but also their emotional and personal characteristics. Results are expected by year's end and will be available to HR professionals for a fee.
"In some cases the innate characteristics of people, like a predisposition to curiosity, can be more predictive of someone's performance in a role than them having a degree in, say, IT or IS or CS," says Talent Analytics' Roberts.
Wanted: A relentless, scientific temperament
Until the recent past, creativity, curiosity and communications skills have not typically been emphasized in IT departments, which may be why most sources said they weren't looking to their operations IT staff to spearhead big data projects.
IIA sees data science as resting on three legs: technological (IT, systems, hardware and software), quantitative (statistics, math, modeling, algorithms) and business (domain knowledge), according to Phillips. "The professionals we see that are successful come from the quantitative side," he says. "They know enough about the technology but they aren't running the technology. They rely on IT to give them the tools."
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