What is your current job/career?
- Director of Analytics and Innovation, Clinical Performance Analytics at Banner Health
How do you use math or mathematical problem solving in your work? How does your math background help you in your job?
- In a few ways:
- Using statistical methodologies, we evaluate if pilot programs were successful and, if they were, what made the program work and how can we quantify the return of the investment.
- Using predictive modeling techniques, we proactively identify patients who are highly likely to be admitted into the hospital to try to prevent the episode.
- Understanding math helps to apply critical thinking to business problems to create a strategy to solve them.
What motivated you to study math?
- I had an early affinity to math’s logic and deductive reasoning. I also had an ability to understand mathematical concepts compared to my peers. So, I had both the skill and a desire to improve and define that skill.
What would you say to a student who is thinking about studying math but doesn’t want a typical teaching career.
- If someone studies pure math, they are almost certain to end up in a teaching job, but most people who like math do not study pure math. Most people who like to study math tend to study within a domain (e.g., economics, finance, healthcare, computer programming, sports analytics, etc.). I would recommend the student to apply their love of math to a specific domain that they are also interested in.
Other advice or comments?
- From a future job outlook perspective, learn a computer language which uses a lot of math and logical concepts and are always needed at various companies.
- Or, as I did, study statistics which of course uses a lot of math. In today’s world, there is tons of data being collected and companies are storing, securing, and curating it which is a large investment. What a company wants in return, is to get something back from that investment in the form of information to make smarter business decisions. The way that is done, is through statistics, data mining, and predictive/prescriptive modeling. In the future, people who understand how to do that will be invaluable and are typically called data scientists.