By Lalitha Venkataramanan, Rachel Levy, and Bill Kolata
| Tamara Kolda
– Ph.D. in applied mathematics from the University of
– Postdoctoral fellowship at Oak Ridge National
– Currently a Distinguished Member of Technical
Staff at Sandia National Laboratories in Livermore, CA
Research interests: Multilinear algebra and tensor
decompositions, graph models and algorithms, data
mining, optimization, nonlinear solvers, parallel
computing, and the design of scientific software
Works with: Electrical, chemical, and mechanical
engineers, computer scientists, and statisticians
Fun fact: Teaches yoga
| Dean Bottino
– Ph.D. in mathematical biology from Tulane University
– Postdoctoral fellowship from the University of Utah,
and National Institutes of Health (NIH) postdoctoral
fellowship at the University of California, Berkeley
– Joined Physiome Sciences and eventually Novartis
as associate director and oncology modeling and
simulation group lead; later led the modeling and
simulation oncology group at Roche Pharmaceuticals
– Led a team for clinical translational modeling and
simulation at Millennium Pharmaceuticals (now Takeda)
Research interests: Mathematical biology, oncology
modeling, and simulation for better decision-making
Works with: Clinical pharmacologists, biomarker
experts, physician-scientists, and biostatisticians
Fun fact: Was executive vice president of a two-person
company, BioAnalytics Group LLC, before joining Novartis
| Gary Green
– B.S. from the University of Idaho
– M.S. from Michigan State University
– Ph.D. from Pennsylvania State University
– Taught for many years in the California State University
– Employed (since 1974) at The Aerospace Corporation,
a nonprofit company that works primarily in national
Research interests: Problems related to launch vehicle
simulation and development, satellite visibility and
proximity, space system performance, satellite-based
navigation and geolocation, and space system threat
Works with: A variety of people with degrees in the
mathematical, engineering, and physical sciences, most
of whom can be classified as systems engineers who,
although grounded in a particular field of interest, are
cognizant of the many disciplines required to build
Fun fact: Conducts a STEM-driven mathematics
enrichment program for elementary students, showing
them that mathematics can be fun and exciting
| Penny Anderson
– B.S. in mathematics from McGill University
– M.S. in computer science from McGill University
– Senior Engineering Manager at MathWorks
Research interests: Numerical analysis and
mathematical software development
Works with: Software engineers, marketing staff,
quality engineers, documentation and user experience
Fun fact: Currently works in the Boston suburbs,
a long way from sunny Barbados where she grew up
| Amy Sliva
– Ph.D. from the University of Maryland, where she
developed probabilistic models to simulate complex
– Tenure track professor at Northeastern University
– Involved in building intelligent systems at Charles
Research interests: Building intelligent systems,
forecasting and predictive modeling, and data science
Works with: Computer scientists, cognitive scientists,
psychologists, and applied mathematicians
Fun fact: Loves the interplay between computer and
political science; has a double major in both and a
masters in international security and economic policy
Graduate students often look to their thesis advisors as their main mentors. Hence, many students want to continue in academia. Recent data analysis by Bill Kolata indicates that the number of graduating students is much larger than can be absorbed by the academic community. The total number of mathematics Ph.D.s awarded in 2013-2014 was approximately 1,187. On the other hand, the number of tenure track positions in math departments filled by students with a Ph.D. was 187 (38 in doctoral-granting institutions). 556 doctoral graduates (44.6%) accepted postdoctoral positions. The 2012 SIAM Mathematics in Industry Report suggests that “University faculty must actively encourage students to consider careers in industry and prepare those students for the very different world they will encounter upon graduation.” The 2015 NSF-IPAM Mathematical Sciences Internship Workshop Report echoes this proposition and discusses recommendations for infrastructure and programs that could increase the number of internships targeting mathematical sciences students. With this in mind, SIAM hosted a panel discussion on careers in industry at the 2016 SIAM Annual Meeting, held in Boston this July. The panel was organized by the authors of this article.
Panelists Tamara Kolda (Sandia National Laboratories), Dean Bottino (Takeda Pharmaceuticals), Gary Green (The Aerospace Corporation), Penny Anderson (MathWorks), and Amy Sliva (Charles River Analytics) discussed their journeys from graduate school to their current jobs and fielded questions from the audience. The panel touched on a few themes, which are highlighted below.
How should students prepare for a career in industry?
The panelists urged undergraduate, graduate, and postdoctoral students to take computer science courses. In particular, achieving programming proficiency in C, Python, or MATLAB is very valuable in demonstrating and testing the feasibility of a research method. Since mathematicians are often involved in data science, the panelists also recommended that students take sufficient statistics courses to enable data modeling. In addition, industrial internships during the summer months of the first few years of graduate school offer students a glimpse into what is required for an industrial career.
What do companies look for during the interview process?
Interviewers at companies look, first and foremost, for relevant technical experience. Equally important is demonstration of mathematical maturity: the ability to look beyond the underlying problem, perceive the big picture, and question the problem formulation if necessary. Moving from one problem to another is often a challenge for students who have spent many years working on the same problem in graduate school. This flexibility in transitioning between different problems is an important component of working in industry. In addition, most industrial mathematicians work on a team with other scientists and engineers. Thus, it is important to learn to communicate well within a team and be a team player. Finally, mathematicians are often asked to explain their work to upper management who may not have the same mathematical background. A short “elevator pitch” targeted towards a specific stakeholder should be relevant and capable of explaining the work in sufficient detail to capture its value.
What would you recommend to a student who is getting their bachelor’s degree in math or applied math and is interested in an industry job?
To make yourself easily marketable, try to pick up a second degree with a major in a complementary subject, such as computer science or a relevant science. Some companies offer programs to mentor young mathematicians that allow them to keep their jobs while pursuing a master’s degree or a Ph.D. If applicable, discussing this option during the interview process is useful.
How difficult is it to give up teaching?
Moving from academia to industry means giving up teaching, and this can be quite challenging. However, the ability to make a difference in the workplace can often replace the joy of teaching. Technical guidance of company interns can have a similar feel to academic teaching. In addition, mentoring junior scientists at the workplace can be quite rewarding. Mentoring can be done formally (through a program at a company or through association with a society, such as the Association for Women in Science) or informally (meeting periodically with a junior colleague).
How is work evaluated at your company?
In academia, a faculty member’s work is evaluated based on his or her teaching and research abilities. In industrial research, technical work is also evaluated on the quality of applied research. Some aspects of this research may be written in peer-reviewed publications or presented at conferences. However, the technical work is more often captured in invention disclosures, which is the first step towards a patent. Technical work also frequently results in some features of software code that can be used internally within a company or commercialized and shared outside. Experience with business strategy, mentoring and community leadership, and professional visibility at conferences and universities are other benchmarks used to evaluate a scientist/engineer’s performance.