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Data scientist has famously been called "the sexiest job of the 21st century," and there can be no doubt that there's a lot of interest in data scientists and the work they do. That isn't surprising: There's something almost magical about being able to predict future business scenarios or find useful facts where others see only rows and rows of raw data. But despite all the hype, data science jobs aren't seeing especially high demand among employers.
The fact is, there's more call these days for data engineers, a less sexy position that typically involves identifying and implementing data analysis tools and working with database teams to ensure that data is prepared for analysis.
You couldn't tell that from Google: The number of searches on the term data scientist has shot up since 2012 and is continuing on a sharp upward trajectory (see Figure 1). By comparison, data engineer gets less than one-third as many searches.
But checking job listings on LinkedIn returns nearly three times as many results for "data engineer" as "data scientist." That isn't a new trend. Figures from job listing site Indeed.com show that since 2006, the percentage of listings on the site for data engineers has held relatively steady at around 1.5% of total job listings on the site, with the exception of a brief upsurge followed by a corresponding downward correction around 2012 (see Figure 2).
Job openings for data scientists are near their historic average today as well, but that's a much lower average, at barely above 0.15% of all listings. And the total number of data scientist positions listed is currently well below the most recent peak in 2013 (see Figure 3).
Skilled data scientists in short supply
The disparity may be partly due to the fact that there are so few true data scientists available to hire. The mix of analytics, statistics, modeling, data visualization and communication skills that data scientists are expected to have makes them something of the proverbial unicorn. If businesses have realized that they aren't likely to find suitable candidates, it would make sense that they aren't bothering to post listings for data science jobs.
It could also be that companies just don't see that much value in hiring data scientists. They generally command large salaries due to their mix of skills. Hiring a data engineer to fix the info plumbing and a team of business analysts trained in self-service software like Tableau or QlikView to ask questions and get answers might make more sense economically.
Businesses are, after all, pragmatic. Glassdoor.com, another job listing site, estimates the national average salary for data scientists to be $118,709. Data engineers make $95,936 on average, while data analysts take home $62,379. Combined, a data engineer and a data analyst may cost more, but they're typically tasked with more general responsibilities that can have more concrete business value, and they should be able to get a lot more done than a single data scientist.
Data science jobs not so necessary?
There's also the question of need. A lot of businesses don't have big, complex data-related questions answerable only by Ph.D.-level data scientists. Many organizations' data problems are much smaller in scale and can be managed in self-service tools that don't require advanced statistical analysis skills or programming knowledge.
None of this is meant to diminish what data scientists can add to an organization. For businesses that have valid needs, they can be game changers. But there has been so much hype about the role that corporate executives could be forgiven for thinking that hiring a data scientist is equivalent to employing a business magician. In many cases, the reality simply doesn't match the level of exuberance.
Data-savvy young people who are considering which way to take their skills may want to take note. If you want a sexy job, become a data scientist. But if you want more job opportunities, and perhaps more job security, becoming a data engineer might be a better career choice.
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