Is the market for data scientists actually deflating?
That’s the depressing conclusion that TechRepublic seemed to reach, after citing data from Glassdoor that suggested pay for data scientists shrunk 1.2 percent (year-over-year) in March 2019. “This is a continuation of a longer running trend—data scientist wage growth has been well below the national average for the last year,” said Glassdoor senior economist Daniel Zhao.
To be fair, it’s not like data scientists are going to starve in the street anytime soon: Glassdoor estimated data scientists’ median base pay at $95,459, which echoes the findings of other organizations. For example, the most recent Dice Salary Survey pegged the average data scientist salary at $95,404 per year (with no change year-over-year).
Nonetheless, it’s clear that something is going on. TechRepublic also cited a blog posting by Vicky Boykis, senior manager for data science and engineering at CapTech Ventures, who suggested that an influx of new people to data science might be leading to an oversupply of talent. “Based on my own participation as a resume screener, mentor to data scientists leaving boot camps, interviewer, interviewee, and from conversations with friends and colleagues in similar positions,” she wrote, “I’ve developed an intuition that the number of candidates per any given data science position, particularly at the entry level, has grown from 20 or so per slot, to 100 or more.”
An analysis of Dice job postings shows that job postings for data scientists rose steadily between 2016 and 2018, with a huge spike early last year—only to collapse back to 2017 levels by summer. Since then, employer demand for data scientists has continued to rise, albeit slowly.
Over the past several years, companies big and small have stocked up on data-science talent. The largest of the large enterprises, such as Walmart, have units dedicated to analyzing petabytes of transactional data for insight; and Wall Street firms are happy to pay $1 million salaries to data scientists capable of generating profitable insights from financial data. Smaller firms without the budget for that sort of commitment are making do with data-analytics tools if they can’t hire a single data scientist.
The highest-paid data scientists have skills that set them apart from others in their field. For instance, those aforementioned finance data scientists must have the “soft skills” necessary to deal with both the C-suite and the in-house analytics teams—a hard task in an environment with big egos and lots of money at stake.
“That pool of [finance-minded data scientist] candidates gets limited very, very quickly,” Richard Pook, an executive search consultant at Dore Partnership who specializes in machine learning and artificial intelligence (A.I.), said at the recent AI and Data Science and Trading Conference in New York.
It’s no different anywhere else, where top data scientists must combine their “core” data-wrangling abilities with whatever specialized knowledge their industry demands. While openings for entry-level positions might be flooded with candidates—leading to that salary-crunching oversupply—businesses always need tech pros with experience and a particular set of skills.
For data scientists, that makes things pretty clear. Maybe you’ve been involved in data science since well before it was popular, or maybe you went to school for it once you began seeing articles about how data-scientist demand would outstrip supply for the next twenty years or so. However you arrived here, it’s clear that, despite the continuing necessity of data science to businesses of all shapes and sizes, you can’t just walk into a data-scientist gig; you need to stand out.
And that means you’ll probably have to focus deeply on the nuances of a particular industry—and its related needs—in addition to your “baseline” data scientist skills. With so much competition for jobs out there, it’s not about “plug and play” into a particular position; you need to show you can tailor your skills and experience to a company’s specific needs.