Big Jobs Guide

This is a compendium of guidance for students in the mathematical sciences who might be considering careers in business, industry or government (BIG). This acronym is an alternative chosen in preference to the usual terms “non-academic employment” or “working in industry”; the first sounds both vague and negative, while the second is too restrictive. The book is very broadly aimed: from students early in their undergraduate years to Ph.D. students, post-docs, and those in visiting or lecturer positions. The authors also address faculty members who might be in a position to advise and support students. “Mathematical sciences” in this book are meant to include mathematics, statistics and operations research.

Motivation for the book derives from two current trends in the job market. Finding a position with a future in the academic world for a new Ph.D. has become more and more difficult. At the same time, the number of opportunities in the BIG world has grown substantially. Students don’t often get advice or encouragement to explore careers outside academia. In many cases advisors don’t have the background or experience to help their students explore careers in the BIG environment. This book is intended to be a first step in providing some guidance all around. While Ph.D. students get some special attention in this book, much of the guidance is useful for mathematics students at all levels.

Many of the topics treated here are standard in good career counseling but the orientation toward mathematics students is particularly helpful. What kinds of jobs are available to mathematically trained students? What skills make a student attractive to potential employers? What courses are especially desirable? What makes a good resume? Are internships available, and how does one learn about them? What are good strategies for getting interviews and then performing well at them?

Some of the book’s best parts are the segments where mathematically trained people employed in the BIG fields offer comments about and descriptions of the nature of their work. These come in short segments interspersed throughout the book and more extensively in a chapter that incorporates personal stories from the BIG Math Network blog.

The book has a great deal of information packed into relatively few pages. This is a good thing because students will find much that is useful in one place. But the authors are trying to reach several different audiences with so much stuff that inevitably the message to any one group gets diluted. Things that are relevant to Ph.D. students are less useful to post-docs and maybe only of marginal interest to undergraduates. So, it’s a good start, but students will probably need more support better matched to their academic background and degree level than what is offered here.

The material on resumes and interviewing is quite useful, but it would have been helpful if more attention was addressed to the issues faced specifically by mathematics students. As the authors note, people in hiring positions have a pretty good idea what students in statistics and operations research bring to the job. However, mathematics itself is often seen as too abstract and impractical. Ph.D.s, in particular, can be very vulnerable in this respect. While a Ph.D. in mathematics may be viewed as especially desirable for a data science job, for example, other potential employers might view the degree with a good deal of skepticism because they regard it as excessively theoretical and too narrowly focused.

The authors also offer suggestions of other places (books, workshops and online resources) where students can get other practical information about finding employment. One excellent source they don’t mention is So What Are You Going to Do with That? : Finding Careers Outside Academia by Basalla and Debelius.

Bill Satzer (bsatzer@gmail.com) was a senior intellectual property scientist at 3M Company. His training is in dynamical systems and particularly celestial mechanics; his current interests are broadly in applied mathematics and the teaching of mathematics.