MachineLearning

Born in United States • Studied PhD in Mathematics at University of Minnesota in Minneapolis, United States • Lives in United States • Occupation Data Scientist

Galileo Galilei said “Mathematics is the language with which God has written the universe.” I chose to have a career in mathematics because I wanted to be a “translator” for the language of mathematics. 

The first time I realized that I might enjoy teaching math was when I was in sixth grade.  I was writing up a solution to a pre-algebra problem for a school newspaper article, and I discovered that I loved breaking the problem down into smaller steps that could each be carefully explained. Communicating a logical and precise solution was beautiful to me.

When I went to college, choosing a major was tough because I was curious about many subjects. What drew me towards math during my freshman year was the idea of becoming a college math professor. A career as a math professor would allow me to combine the challenge of solving math problems as well as communicating the results.  Furthermore, the fact that mathematics could be applied to a variety of fields appealed to my widespread curiosity. During college, I studied applications of math to some familiar and loved subjects (such as music) as well as some new and interesting ones (such as computer science). I majored in math and minored in physics and computer science with the goal of pursuing a Ph.D. in applied mathematics upon graduation.

Graduate school was very different from my undergraduate studies. The coursework was more demanding, so I had to improve my study habits, and research required that I develop an entirely new set of skills. The nature of research was very different from the syllabus structure of problem sets and exams in a course. Since my goal was to solve a problem no one had ever solved before, it required a creative and flexible approach, one that emphasized the exploration, experimentation, and steady refinement of ideas.  But perhaps the most important lesson I learned was that there is no single “correct” way to be a mathematician. I saw that fellow students succeeded by developing a process of learning and research that worked for their unique set of talents and interests. I, too, had to develop such a process, even though it was an arduous and intimidating journey, fraught with a lot of trial and error. Ultimately, though, the effort was worth it because it built my self-confidence.

Since my goal was to solve a problem no one had ever solved before, it required a creative and flexible approach, one that emphasized the exploration, experimentation, and steady refinement of ideas.  But perhaps the most important lesson I learned was that there is no single “correct” way to be a mathematician.

At the end of graduate school, I had an unforeseen change of plans. My goal had always been to get a tenure-track job (which is the career track to a permanent academic position in America) at a local school. However, since no local positions were open the year I was graduating, I had to consider the trade-offs between my geographic location and the type of job I wanted. If I didn’t relocate, I would have to broaden my job search to include non-academic jobs (which I didn’t know much about) and temporary academic jobs (which had more uncertainty). It was scary to consider changing my long-held career plans, but I had an established support system of family and friends locally who were an important part of my life. After extensive deliberation, I accepted a two-year faculty position at a local school and began investigating non-academic career paths.  

Luckily for me, jobs in data science were starting to surge around the time I started looking at industrial jobs. Companies were looking to hire employees who understood complex statistical and machine learning algorithms and could write computer code.  Data science was a great fit for my interests and skills – I had a lot of programming experience and was willing to learn whatever additional mathematics I needed for a job – so I began looking for jobs where I could use and further develop my technical skills.  

My first industry job was building statistical models for pricing policies at an insurance company, and from there I segued into data scientist and software developer roles. Although the domains are different and the mathematical techniques I use vary, my jobs generally have consisted of formulating the mathematical problem, writing the code to train the model and implementing the solution, and explaining the results to business stakeholders. I’ve worked as a data scientist at several companies on problems with diverse applications: energy, finance, supply chain, manufacturing, and media.   Although the details of my professional life are different than if I was a math professor – the work is interdisciplinary and team-oriented – I still get to be a “translator” of mathematics. 

Even though my career path has gone differently than I originally planned, I am happy with the unexpected directions it has taken me. Keep in mind that the best career path is not about what the majority is doing or what others advise that you “should” do: it is the path you create for yourself.

Published on March 12, 2025.

Photo credit: Catherine Micek