Employers’ hunger for mathematicians and data engineers seems insatiable, according to our November breakdown of data from Burning Glass’s NOVA platform, which analyzes millions of active job postings.
In fact, there’s been precious little shift in the top rankings from last month’s analysis: mathematicians and data engineers placed first and second, respectively, followed by computer scientists (up from fourth place last month). Job postings for mathematicians grew 91.7 percent year-over-year, easily outpacing every other position on the list by a considerable margin.
Here’s the full chart:
As with previous months, the primacy of mathematicians and data engineers suggests that companies are intent on assembling a workforce that can efficiently evaluate, clean, analyze, and generate useful insights from data. That conclusion is reinforced by other jobs on this list, including data scientists and various kinds of analysts—it’s clear that wrangling data is a key part of companies’ strategy.
For those interested in pursuing these professions, the other good news is that pretty much every industry—from heavy manufacturing to entertainment—needs number-crunchers. Your value is only enhanced if you develop highly specialized skills and rack up important certifications. For example, a data analyst might target certifications such as CCA Data Analyst, SAS Certified Data Scientist, or the Data Science Council of America Certification.
Within tech, mathematicians are often tasked with wrestling massive amounts of data, which means that at least some of them will end up using the tools ordinarily reserved for data scientists and infrastructure engineers, including Hadoop, containers, and Kubernetes. For those companies with businesses centered on the Web, mathematicians might spend a lot of time crunching numbers derived from Google Analytics and similar monitoring platforms.
When it comes to programming languages and data analytics, many experts suggest you start by learning Python, which is supplanting R as the programming language for number-crunching applications (plus, since it’s a generalist language, you can use it for many other things, as well). Python is also replacing various tools in math-heavy professions such as finance.
Given many companies’ intense need for data, we don’t anticipate any radical shifts in these rankings anytime soon. Even if you’re not in a profession that tends to emphasize math or data analysis, knowing a few analytics platforms might prove useful to your future career arc.
