Tell me about yourself / What did you do after graduation?
After graduation, I held several interesting, if brief, positions. I worked at a technology startup and as a legislative assistant in the House of Representatives, both part-time and concurrently for several months. I was then a statistician at the Science and Technology Policy Institute for two years. I left to be Director of Data Science at Impact Research, a small firm doing data science for auto safety. After two years there, I left for In-Q-Tel, a nonprofit at the nexus of venture capital, startup technology, and national security, where I stayed for 6 years. I left in 2023 to start a business, Foundation, whose mission is to help organizations make faster decisions with less uncertainty by measuring narratives that drive economic outcomes. In 2023, I also completed my Ph.D. in Computational Science and Informatics at George Mason University.
Why did you choose Georgetown?
Truly, it was circumstance. (But I am so happy I did it!) I had been a math minor as an undergrad and was hoping to go into a Ph.D. program in economics. Rather than taking more undergraduate math classes, I discovered that MAST would let me get a master’s degree. Its flexibility and not requiring a thesis were perfect for my goals at the time, even though my plans changed.
How did the MAST program prepare you for your career?
I cannot overstate how valuable a rigorous education in probability and statistics has been for me. In today’s world, so many people are getting into machine learning and data science, and you need a way to differentiate yourself and add value that others can’t. Yes, you need to know how to program. But that isn’t a differentiator. Most people simply don’t have the background to engage with the broad swath of methods in use today. But my education allowed me to engage with all areas of machine learning and link them back to more basic principles. That gets me unstuck, finds creative solutions, and gets to deep issues faster than many others coming from different disciplines. Frankly, MAST’s probability and statistics curriculum is so rigorous that I’ve found that I know more theory than many graduates of other master’s-level statistics programs.
What’s your advice for alums?
Your education will leave you well-prepared for the technical aspects of a beginning career in data science. Lean into learning soft human skills like empathy and communication, and you will be extremely well-placed for the remainder of your career.