Is data science still sexy?
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“What is data science?” The answer to this question changes every year. In Oct 2012, the Harvard Business Review named data scientist “the sexiest job of the 21st century”. Ten years after, is data science still sexy?
It is still in demand, and the compensation is good compared to many other jobs. But I don’t think it is sexy. Looking back now, I feel it was rather ambiguous in 2012 than sexy. More people are doing data science now, and it is not as mysterious. People also know that a data scientist is not a unicorn with skills ranging from engineering to analytics to modeling. Data science practitioners usually focus on a specific area.
Today’s data science is different from 10 years ago. What has changed? Here are the changes I experienced.
1) More DS education programs
As a result, you don’t need a Ph.D. to be a data scientist. Many data scientists today have MS and BS degrees, and that works just as well. There is a trend shifting from a Ph.D. degree to a data science master’s program as a faster route to the workplace.
2) Title creation and shift
Many titles today didn’t exist 5-7 years ago, such as machine learning engineer and applied scientist. There were many new titles created.
Also, there is a shift in titles. It started with some companies changing their analyst titles to scientists. Other companies found it hard to hire people under the “analyst” title as other places gave people a fancy title. As a result, everyone started to change their titles to scientists. Some experienced data scientists from a research background moved to similar positions with different titles, such as research scientist and quantitative researcher.
3) Title inconsistency across different companies/industries
The same title at different companies can do different things. What an analyst at Google does may differ from what an analyst at Uber does. Some titles may only exist in a small group of companies. For example, not every company has an “applied scientist.”
4) Better job definition (within an organization)
Even though the titles are inconsistent, the job definition for a specific title at a company is pretty clear. It was not like five years ago when a job description was a pile of technical keywords from engineering to modeling. Disregard the title; if you read the job description, you may know what the role does. There is also better support and career path in data science.
5) Standard interviewing process (streamlined)
Now, there is a relatively standard interviewing process. Before, data science interviews differed a lot for different companies. Some companies asked you to do whiteboard coding, but some didn’t even ask programing questions during the interview. Most organizations have created a streamlined and transparent interview process to access the skills needed for a specific role in data science. Companies will also clarify the role and responsibilities, tool usage, and day-to-day work during the interview.
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