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Data scientists are the rare alchemists of the data-deluged digital age.
They extract value out of the massive quantities of meaningless data that we generate every day, and competition for their skills is fierce. Job postings for data scientists ballooned by more than 15,000 percent between 2011 and 2012, and entry-level salaries start at $110,00 to $120,000.
Rather than choosing the comfort and security of a well-paid position at a large company, some of these coveted technicians choose to forge something of their own. A number of the most successful data startups have data scientists at their helm.
VentureBeat talked with a series of data scientists who went on to become founders and CEOs to learn more about how their background influences their leadership, product, and business strategies — and what it takes to succeed in an increasingly competitive, data-driven world.
All these data scientists-turned-CEOs put a heavy emphasis on data in their own business. They make data a core part of their strategy, operations, and decision-making process. Ultimately, data is only as valuable as what you do with it, and as self-described “data nerds,” these CEOs have an edge.
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Shashi Upadhyay has a Ph.D in physics from Cornell. He analyzed humongous datasets as part of his doctoral research. After graduation, many of his classmates and colleagues took jobs on Wall Street, where they were well compensated for their knowledge of math and statistics.
Upadhyay accepted a position with McKinsey Consulting (a global management consulting firm), where he spent six and a half years advising on sales and marketing problems before founding Lattice Engines.
“As the world has gone digital, data volume has exploded, and retailers tend to have humongous amounts of data,” Upadhyay said in an interview. “I realized there was an opportunity to connect the dots between my experiences. It is hard for most companies to put together a data-science team and compete in the war for talent, and I thought instead they would look for automated solutions.”
Lattice Engines bills itself as “big data for big sales.” Its platform analyzes data and delivers real-time reports with specific data to sales representatives, who can use the information to generate leads and close deals. The engine uses predictive analytics to help sales people anticipate their customers’ behavior.
Upadhyay said that founders and execs of data companies must be the “masters” of three domains: They must have unique subject matter expertise, an understanding of machine learning, and the capability to build systems that can scale. And it’s rare to find a leader with a grasp of all three.
“If you spend all your life analyzing data, like I have, certain things become muscle memory,” he said. “You know what is important to the end users, what the problems are, what is doable, and how long something will take. What makes us differentiates us is we have all three of those pieces.”
Upadhyay’s data science background is also important for recruiting data scientists to the Lattice team and ensuring they are productive and happy employees.
“Other companies make the mistake of bringing in data scientists and treating them like developers, but they are not the same,” he said. “Data scientists care about having an impact on the business, but companies systematically underinvest in training them in the domain and forming a linkage with other parts of the business.”
Thomas Thurston is something of a renaissance man when it comes to data science. He has an MBA and a law degree and is a member of Harvard Business School’s Forum for Growth and Innovation.
Thurston spent a stint working at Intel Capital, serves as the chief technology officer and fund manager of the Ironstone Group (a venture firm that uses data science to make investments), and is the founder and CEO of Growth Science, which uses data to predict if businesses will survive or fail.
“I think of data science as a way of thinking about the world in terms of hypotheses, testing, confidence, and error margins,” Thurston told VentureBeat. “A background in data science tends to help CEOs ask better questions and get better feedback, because it brings conversations down to a level of reality and practicality. Facts, data, and probabilities can have a way of removing the ego, politics, and hand-waving from a conversation.”
Thurston said that he favors hard data over more intuitive considerations. Like Upadhyay, he is a proponent of challenging all ideas until they are backed up with data and not taking anything for granted.
“It’s not that I don’t value intuition or more ‘soft’ inputs – sometimes they’re so important they can override everything else,” he said. “It’s just all too often in data science that you see intuition, anecdote, and feeling get turned on its head by actual data. Everyone thought the world was flat. It looked that way. It felt that way. It was intuitive. It was also dead wrong. I find it corrective to try to keep this in mind. Like it or not, I can be wrong at any moment, so I must be willing to adapt.”
Thurston said his favorite “data science moments” are when he learns something that flies in the face of conventional wisdom, and that these can become significant commercial advantages.
However data science and the advantages it brings have yet to make their way into more mainstream businesses. Both Thurston and Upadhyay expect that the evolution of this field will involve making data science more accessible to smaller, less tech savvy businesses.