When it comes to big data, one thing seemingly everyone can agree on is that organizations face a shortfall of data science talent. After all, the ideal data scientists aren't just wunderkinds in advanced mathematics and statistics, they're creative, non-linear thinkers with excellent communication skills. In popular parlance they're unicorns — magical creatures that don't exist.
Research firm McKinsey has predicted that by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and demand of deep analytic talent.
Bob Rogers, chief data scientist, Big Data Solutions, Intel, might just fit the unicorn bill. Rogers, who holds a PhD in Physics from Harvard University, got his start studying supermassive black holes. He co-wrote a book on time series forecasting using artificial neural networks, which led him to co-found a quantitative futures hedge fund that leveraged large amounts of historical and streaming tick-by-tick data from markets. He's also helped a medical technology firm revolutionize glaucoma diagnostics and co-founded another business to help the healthcare industry extract data from electronic health records.
For the past year, he's been the chief data scientist at Intel's Big Data Solutions, which started as a project to better leverage the data inside Intel, but has grown to encompass helping Intel clients better understand analytics and data problems. For instance, he's been working closely with the Knight Cancer Institute at Oregon Health & Science University (OSHU) to help develop the Collaborative Cancer Cloud, which aims to make it possible to sequence an individual's cancer, analyze it and formulate a precision treatment plan within 24 hours.
You'll never find that data science unicorn
In the process, Rogers has helped to define what Intel looks for in its data scientists, and it's not unicorns who have a background in math, statistics, physical science or hard science; the ability to write production-level code; and the ability to talk to business people in their own language.
"You don't have to be a unicorn," he says. "We're looking for people who have one of the major skill sets and some comfort level with the others — the ability to be creative, handle ambiguity and communicate well. One of the key outputs of that kind of thinking is the ability to characterize what's important to others."
Think in terms of data science teams with diverse capabilities that can complement each other, rather than seeking to hire individuals that can do it all, Rogers says.
"It's true that having advanced knowledge of mathematics and programming is fantastic background for a data scientist," he says. "But, in any company, you won't find just one data scientist doing it all — just like Michael Jordan couldn't have scored so many points without Scotty Pippen at his side, data scientists all bring their own skills to the table that together build an ideal team."
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