Spencer Segal '21

Spencer Segal is a two-time University of Pittsburgh alumni who graduated with a Bachelor of Arts in Economics and Sociology in 2020 and from the Master of Quantitative Economics (MQE) program in the spring of 2021. He currently works as an Associate Economist at Moody’s Analytics in the Quantitative Research Group.

Q. How has the MQE Program provided you with the skills needed to succeed in your current position as an Associate Economist at Moody’s Analytics?

A. The MQE program provided me with a quantitative skill set that has put me on the path to succeed in my current position as an Associate Economist. My work as an Economist requires me to approach complex coding problems with an analytical mindset, a skill honed by my work in the program.

Q. What did you love learning about the most during your time in the MQE program?

I really liked learning about the different machine learning techniques. Prior to the MQE program, I had no experience in machine learning. The professors created an engaging and intellectually stimulating learning environment that allowed me to really be able to dive into learning the nuances of the various machine learning methods and how they are applicable to the real world.

Q. How did you implement skills from the MQE Program into your Capstone Project?

The capstone project was the culmination of everything I learned in the MQE program. It allowed me to implement all the skills and theories that I had learned over the course of the program into a real-world problem. That real world applicability was priceless. I used programming skills, machine learning techniques, communication skills, and economic theory to estimate salaries for minor league hockey players for the Pittsburgh Penguins.

Q. How did the MQE program help prepare you for real-world job/career experience?

The MQE program helped me understand that real world analytical problems are not so cut and dry, but can be very messy and unexpected. The MQE program prepared me to tackle problems no matter how large or small they may be.