Ten years ago we published the article “Data Scientist: Sexiest Job of the 21st Century.” Most casual readers probably remember only the “sexiest” modifier — a comment on their demand in the marketplace. The role was relatively new at the time, but as more companies attempted to make sense of big data, they realized they needed people who could combine programming, analytics, and experimentation skills. At the time, that demand was largely restricted to the San Francisco Bay Area and a few other coastal cities. Startups and tech firms in those areas seemed to want all the data scientists they could hire. We felt that the need would expand as mainstream companies embraced both business analytics and new forms and volumes of data.
At the time, we defined the data scientist as “a high-ranking professional with the training and curiosity to make discoveries in the world of big data.” Companies were beginning to analyze voluminous and less-structured data like online clickstreams, social media, and images and speech. Because there wasn’t yet a well-defined career path for people who could program with and analyze such data, data scientists had diverse educational backgrounds. The most common qualification in our informal survey of 35 data scientists at the time was a PhD in experimental physics, but we also found astronomers, psychologists, and meteorologists. Most had PhDs in some scientific field, were exceptional at math, and knew how to code. Given the absence of tools and processes at the time to perform their roles, they were also good at experimentation and invention. It’s not that a science PhD was really required to do the work, but rather that these individuals had the rare ability to unlock the potential of data, wading through complex, messy data sets and building recommendation algorithms.
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