What are our alumni doing?
Nate Burch
Graduation Year: 2006
Major(s) and minor(s): mathematics major, Statistics minor
Post-grad education: 2008 Masters in Mathematics, Colorado State University
2009 Masters in Statistics, Colorado State University
2011 PhD in Mathematics, Colorado State University
Current position: Model Validator
Employer: State Farm Insurance Company
The most helpful aspects of my undergraduate education in mathematics were: The first thing that comes to mind is learning how to experiment with mathematics. Mathematics really started to “click” for me when I discovered the ability to experiment and tinker - look at an example, run a simulation, create a visualization of a theorem, etc. This experimentation-based approach has stayed with me my entire career and was fostered by the curriculum, research opportunities, and professors at GVSU. The second thing that comes to mind is appreciating the value in exploring a problem from multiple perspectives. As an undergraduate, I was very interested in the intersection of statistics and mathematics. I have fond memories of my GVSU professors, from both departments, helping me to make connections between the two fields. Finding ways to view and discuss a problem from multiple lenses has been incredibly valuable professionally and is just plain fun!
The advice I’d offer to current undergraduate students in mathematics: My advice is to remain curious, ask questions, and keep learning. The role that mathematics plays in our world is growing and evolving, and evolving quickly. Your mathematics degree has given you a set of tools to help you think critically, ask good questions, and learn. Use those tools! If there is something you encounter in your work that you don’t know or don’t understand, embrace that as an opportunity.
In a typical day in my work: Our group validates all statistical, actuarial, and machine learning models that are created for use at State Farm. These range from regression-based propensity models for our marketing group to deep learning models for our advanced analytics groups, and everything in between. If it’s a model, we’ll validate it! Essentially, we serve as independent peer reviewers of the entire model proposal - we verify an appropriate model is chosen, that model is developed properly, and that associated risks to the enterprise are well-understood. Our day-to-day varies from pulling data with SQL, performing EDA with Python, building challenger models, reading research papers to stay up-to-date on cutting edge machine learning models, and writing reports.
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