UPDATED 08:30 EDT / NOVEMBER 29 2013

The changing job market for data scientists : Advice from top bosses

Data scientists are now a crucial part of the tech industry. As more people use technology ranging from home and office PCs to laptops, mobile devices and the internet, more and more data is being generated. It would seem that data scientists are needed more than ever to make sense of all this amassing information.

theCube, SiliconANGLE’s premiere video production, has been conference-hopping all year, speaking with top executives and industry leaders on the changing landscape of data-related professions.

We’ve gathered quotes from theCUBE’s recent interviews with experts who discussed a variety of related topics–from finding the right people to work as data scientists, to the challenges data scientists face in today’s professional market.

Requirements for data scientists


Hillary Mason of Accel Partners shared her fascination with data science, as it helps answer the question, “Can I understand something about the world as [a] whole from [the data] that helps me as an individual?”

She added that there will never be too many people who can make data analysis-based decisions, and that data scientists require “a communication ability and domain knowledge. It is about code and engineering, someone handing you a messy data set and you can get something out of it.”

Aside from being able to communicate, Mason stated that another important aspect, one that is hardest to find in candidates, is the ability to tell a story or the ability to relay information in a way that is interesting to others.

New data-powered roles


At #BigDataNYC, Bill Schmarzo, CTO of the EIM Service Line for EMC, expects more data-powered roles in the future, such as Chief Analytics Officer and User Experience Officer. He recommends that the person best fitted for these data-powered roles are economics majors as “[they] can look at it from a risk and compliance perspective,” not just a monetary one.

“You’re going to have all these new roles,” he said. “I’m not sure where they go and we’ll learn as we go, but those roles are critical.” He said they will help determine how to get more data, how to protect it and how to get more from it.

As for data artistry, Schmarzo stated that it is a very interesting aspect but he does not consider it as a role. To him, it’s more of a characteristic that a data expert should possess.

A business background


Abhi Mehta, CEO of Tresata, sees data scientists as those having a good business background. “[Y]ou cannot be a good data scientist without having business knowledge to use this massive amount of data, and to make sure [that] if I brought together social, mobile, geo and transactional data, what problem should I solve. You can’t answer that question without domain knowledge,” Mehta stated.

He added that that technology can fill the knowledge gap on the other end of the spectrum. He predicts that machine learning solutions will eventually automate the entire process of aggregating and processing data, enabling business analysts to make good decisions faster.

The rise of the Chief Data Officer


At IBM IoD 2013, Dave Laverty, Vice President of Big Data & Analytics at IBM, stated that companies are now reorganizing to become more data driven. As proof is the creation of the Chief Data Officer (CDO) title. He sees CDOs as facilitators that are able to understand the value of the data organizations have and how to use it more effectively.

He remarked that there are only five percent of organizations with CDOs but he expects it to grow to 50 percent by 2015. “[O]rganizations need to think about a new architecture,” he said. “How [you] architect for the future [should] play a key role in that.”

Framing the problem for data scientists


Jake Porway, Founder & Executive Director at DataKind, thinks that the biggest aspect in data science is not finding data scientists but asking the right questions. “I thought the biggest thing we’d be dealing was data related, that we were going to bring all these data scientists in,” he said. “But frankly, the biggest aspect is actually the framing of the problem, really finding the question. As any good data scientist will tell you, it’s not so much about the data, it’s the question you start with.”

During his interview on theCube, he was asked if he agreed with Jeff Hammerbacher, a famous data scientist, when he stated that the “best minds of my generation are thinking about how to make people click ads.”

Porway stated that it’s a very important tool to have and business is a very important aspect. “I don’t like people who go one side or another,” he said, “who vilify business.”

Data scientist roots


Michael Kowelenko, Assistant Professor at North Carolina State University, was asked his opinion about whether or not everyone can be a data scientist if given the right tools. He stated that, in business school, people were trained to be data managers, to understand the technology and to leverage it to make better decisions. “We need you to know how to ask a good question,” he said “and extract the data to answer it. You need to know how to converse to data scientists [to get the data to you].”

As for his tips for teenagers unsure of their career path, Kowelenko asks, “Do you like problem solving? If you like it, you will like a career where you put data together and solve puzzles. If you’re naturally curious and you want to find out why the things work the way they do, Big Data is the right career choice.”

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