DSI Alumni Download: Q&A with Thompson Bliss, Data Scientist for the National Football League
Thompson Bliss has always loved watching professional football, especially his hometown team, the Oakland Raiders. He entered the master’s program at the Data Science Institute at Columbia University with the hope that he might analyze data for the National Football League (NFL) one day, but he knew how competitive it is to get such an opportunity. This month, Bliss began working as a data scientist for the NFL. Here, he discusses what he will do in his new job, how he got it, and how the DSI master’s program helped him land his “dream job.”
Tell us about your new role and how you use data science.
The National Football League is always interested in using data to understand and enhance its on-field product. I assist the football operations department in analyzing aspects of the game relating to competitiveness, pace of play, and officiating. I am responsible for week-level and season-level statistical reports, improving the current statistical models for game evaluation, and other analytics projects that serve the football operations department. My daily job functions will involve machine learning and data visualization, much of which I learned in courses at DSI.
How did you get this opportunity?
Working for the NFL is a dream job for me. My family was season ticket holders for the Oakland Raiders from before I was born up until this most recent season. I have not missed watching an Oakland Raiders game on TV or in person since 2009. I watch approximately six to nine hours of football per week in addition to the Raiders games during the NFL season. I was lucky enough to get a summer internship at the National Football League via its online application system. After making a good impression with them over last summer, I was given an opportunity to return for full-time work.
Why did you choose Columbia's Data Science Institute for your master's degree?
I grew up in Oakland and graduated from Oakland Technical High School. For undergrad, I went to University of Wisconsin at Madison, where I double-majored in physics and astrophysics with minors in computer science and mathematics. I knew I wanted to do a degree in data science when I took courses in matrix algebra, mathematics for machine learning, and optimization. I enjoyed using data to solve problems and understood how it could be applied across a multitude of fields. When looking at my options for data science programs, I chose Columbia DSI due to its strong academic reputation, its career services, and its excellent location. I came to DSI knowing that sports analytics was a possibility, but a difficult field to be hired in. I have always loved math and computer science, so I figured if I did not have an opportunity to get a job specifically in sports, I would be happy in any other sort of data science role I’d end up in. I am extremely happy that I was able to get an opportunity to work with football data.
What were the highlights of your DSI experience?
I very much enjoyed the Theoretical Machine Learning course with Professor John Paisley. Additionally, before the program started, I certainly expected to make strong career connections with my fellow classmates, but I was pleasantly surprised by the friends I made in the program, who I still remain close with.
Do you have any advice for current and prospective DSI students?
Make sure you show your interest in data science by doing projects in the specific field of your interest. When interviewing with the NFL for my internship, it was advantageous that I had created a data visualization project analyzing NFL data while at DSI, which I discussed during my interview.
Media Contact: Robert Florida | firstname.lastname@example.org | 201 725-6435, mobile