Harvard Business Review called it the sexiest job of the 21st century.
The job title data scientist was coined at Facebook and LinkedIn around 2008. DJ Patil, who later became the first U.S. Chief Data Scientist under President Obama, and Jeff Hammerbacher, who led data teams at Facebook, are both credited with popularizing the term.1
The role combined skills from statistics, computer science, and domain expertise. What distinguished it from earlier statistical or analytical positions was the scale of the data and the expectation that the practitioner could write code, build models, and communicate findings to business audiences.
In October 2012, Thomas Davenport and D.J. Patil published an article in the Harvard Business Review titled "Data Scientist: The Sexiest Job of the 21st Century."2 The article described a role that barely existed a few years earlier as the most sought-after position in business. Companies were generating data faster than they could analyze it, and the people who could make sense of the data commanded premium salaries.
The article accelerated demand. Universities launched master's programs in data science. Online platforms like Coursera and edX offered certificates. By 2018, the U.S. Bureau of Labor Statistics was tracking the occupation.
The title proliferated faster than the profession could define itself. A 2019 survey by Anaconda found that data scientists spent roughly forty-five percent of their time on data preparation and cleaning rather than on analysis or modeling.3 The gap between the title's prestige and the daily reality of the work became a recurring theme in industry discussions.
A decade after Davenport and Patil's article, the same publication revisited the title. In a 2022 follow-up, the authors acknowledged that the role had fragmented into subspecialties, including machine learning engineer, analytics engineer, and AI researcher.4 The job title that barely existed in 2008 had, by 2022, already begun splitting into roles that did not exist in 2012.