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Data Science: The Path to the C-Suite

Gideon Mann-1819In this special guest feature, Gideon Mann, Head of Data Science at Bloomberg, sets the stage for a new wave of data scientist CEOs. This structural shift may not be for reasons you think. Gideon is the Head of Data Science at Bloomberg, guiding the strategic direction for machine learning, natural language processing, and search on the core terminal. He is part of the leadership team for the Office of the CTO. He joined Bloomberg from Google Research. At Google, in addition to academic research, his team built the core middle-ware libraries for small-to-medium sized machine learning, and publicly released the Google Prediction API, Smart Auto-Fill for Sheets, and coLaboratory (open-sourced as part of iPython / Project Jupyter). Mann graduated Brown University in 1999 and subsequently received a Ph.D. from The Johns Hopkins University in 2006. His focus at Hopkins was natural language processing with a dissertation on multi-document fact extraction and fusion. After a short post-doc at UMass-Amherst working on problems in weakly supervised machine learning, he started working at Google Research in NYC in 2007.

Over the next few years, we may be in for a wave of data scientist CEOs. While we all realize how vital top data scientists can be to an organization, this structural shift may not be for the reasons you think. This isn’t because data scientists are able to wring efficiencies out of sluggish businesses, or because they have the strong quantitative skills necessary for the role—but rather, stems from the trend that businesses today are becoming more data-driven in nature.

In many ways, data science is similar to what finance was in the 2000s. During that time, finance became a key profit center across many industries, which led to a wave of CFOs rising to CEO positions. Today, as data continues to become a core profit center for many businesses, we are seeing more and more data scientists taking the helm as CEOs.

Not only are we seeing data provide unique strategic advantages to companies in more traditional industries, but we are also seeing rapid growth in the number of companies in the business of data.

One great example of a new data business is Premise. Premise can collect macro-economic indicators more quickly than traditional surveys and government methods. It uses crowd-sourcing and micro-payments to collect point-of-sale costs for goods globally, and then synthesizes these point-estimates into a reliable indicator. Premise is purely a data company, and the best-equipped leader to run this business is a data scientist. Here at Bloomberg, we’ve seen first-hand the value that Premise and other data-centric companies bring to our financial clients and so, we expect companies like these to experience continued growth.

Apart from the growth in new data-centered businesses, existing sectors will be entirely remade by data. As an example, autonomous cars have the potential to wholly remake the auto industry, and building autonomous cars requires sophisticated machine learning and data in order to train these models. From this perspective, Tesla’s AutoPilot feature is brilliant. While it’s not an autonomous car, customers who use AutoPilot generate invaluable training data for Telsa, which may give a head-start in making a fully autonomous car. To get the most of a data-focused strategy, like that of Tesla, the direction must come from the top.

Data scientists are no longer confined to a back end department. The problem-solving skills they develop throughout their careers allow them to approach problems with the goal of generating data and using it to yield long-term strategic value. With a data scientist in the C-suite, businesses would be emphasizing data as a new strategic resource to maximize its benefit. A data scientist CEO wave, given our data-driven world, might be on its way.

 

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