Main image of article Mathematicians a Hot Focus of Nationwide Job Postings

Companies really want mathematicians and data engineers, according to the latest breakdown of data from Burning Glass’s NOVA platform, which analyzes millions of active job postings.

Indeed, mathematicians enjoyed an 80.6 percent increase in postings over the past twelve months, well ahead of data engineers with 54.8 percent. Even data scientists—a very hot position right now—only saw 25.9 percent increase in job postings year-over-year. Software developers and engineers, which are traditionally roles that experience strong growth, saw a noticeable but unspectacular rise over the past year.

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This is also a significant change from our past rundowns of Burning Glass/NOVA data. For example, in June, data engineers topped the list, followed by actuaries, computer scientists, video game designers, and geographers/GIS specialists; at the time, mathematicians sat around midway down, between UI/UX designers and web designers.

What’s to account for mathematicians’ meteoric rise? It’s probably the same reason why data engineers, GIS specialists, data scientists, and data architects tend to always take top positions in the Burning Glass/NOVA dataset: companies have an increasing hunger for professionals who can analyze and understand data, and math is a huge part of that.

Moreover, mathematicians are vital for pretty much every industry and discipline, from engineering and chemistry to physics. Their underlying skills not only apply to “old school” tasks such as straightforward data analysis or mathematical modeling; they’re also increasingly vital for cutting-edge disciplines such as artificial intelligence (A.I.) and machine learning, as well as autonomous-driving software.   

Of course, it’s not just about mathematical skill; depending on the industry and discipline they choose to pursue, mathematicians must master other software packages and skills in order to prove truly useful to an organization. For example, mathematicians who gravitate toward data engineering must often learn how to effectively utilize Apache Spark, Scala, Docker, Java, Hadoop, and Kubernetes.