Communication and personality.  These are two of the skill sets you’ll need to develop if you’re thinking about becoming a data scientist, according to two industry veterans from Greenplum.  Wikibon analyst Jeff Kelly sat down with Derek Lin and Noelle Sio, two data scientists currently working on big data solutions with EMC subsidiary Greenplum.  During their interview at theCube at EMC World’s Data Science Summit mini event, Kelly took the opportunity to delve into an important area of the big data buzzword–how it actually translates to a career path to fill the growing gaps around emerging big data technology.

The role of the data scientist is something we’ve been exploring for a while, and its definition is as elusive as the term “big data” itself.  The result is a pretty broad gray area where data scientists are carving out their own niches across a wide range of industries, the common denominator being their ability to break down company problems down to the data and build up a solution for business implementation.  That requires keen communication skills, as you have to work with the business leaders, and also understand the math behind the data.

“Sometimes [businesses] think they think they know what solution they want, but for us, we have to uncover their pain points to see how those problems manifest themselves, so we can bring it to the path part to understand it on our terms,” Sio explains.

It’s very much about balancing the left and right sides of the brain, which is a departure from the traditional career paths we’ve previously seen for mathematicians, statisticians and even engineers.  And that requires certain personality traits that are willing to think outside the box.

“[Data scientists] are very curious, persistent and tend to be very creative too,” says Lin.  “The problems we’re working on are new problems.”

New problems for new careers.  As a self-proclaimed problem junkie, Sio thrives on the excitement of new challenges as a data scientist.  “If you give me something, the gauntlet is thrown,” she smiles.  “If I know how to solve it immediately, it wouldn’t be fun.  These are things that haven’t been solved before.  It bothers you and nags you to get to that next step.  It’s so interesting and the payoff is great.

“It takes a special personality type that craves difficult, challenging things, and wants to learn something every time.”

Lin and Sio certainly make data science seem like an exciting trip, and that enthusiasm has led them each down a winding path traversing fraud detection in the finance industry and quality assurance in the broadcast media space.  Indeed, data scientists are finding that their varied skill sets make them adaptable in an era where big data has big demands.

Which begs the questions, how does one become a data scientist and is my current job at risk? Sio would say not to worry.  “You can’t start planning for what will happen until you know what’s happened.  Find what about [data science] is interesting to you…start thinking about expanding your skills in math if you’re interested in mathematics.  Choose something adjacent to your skill sets.”

Education will also play an important role in supporting the growing demand for data scientists, as Lin points out.  He’d love to see more programs at the university level as well, encouraging a stronger push in engineering, science and math.