Tell me about yourself / What did you do after graduation?
I did not have a strictly “mathy” background. My undergraduate degree is in international studies and Spanish, and then I spent several years abroad working in economic development (as a Peace Corps Volunteer in Ecuador, a Fulbright Scholar in Portugal, a Boren Fellow in Turkey, and as a J-PAL research associate in Mozambique). I had previously completed an MPA and MA in Economics, so I had done my calculus and linear algebra sequence and some econometrics courses, but that was it. I wanted to pursue more rigorous quantitative tools while simultaneously working full-time.
It took me three years to complete the degree part-time, and it was intense, but it paid off. After just one year in the program, people at my organization (the Inter-American Development Bank, or IDB) heard about my background through the grapevine. I was promoted as a data scientist for a vice presidency at the IDB, where I worked on several awesome projects using predictive modeling and network analysis. Outside the IDB, I also led several courses on machine learning and coding in R, and I was twice a speaker at the R|Gov conference.
Now I’m a Ph.D. student in statistics at Carnegie Mellon University, which is an ideal setting for combining statistical theory with useful applications in machine learning, industry, and public policy. I am so honored to be a Georgetown alum and thanks to my professors and the MAST team, I am living my dream.
Why did you choose Georgetown?
One, Georgetown’s MS-math/stats program is the perfect blend of theory and application. As its name suggests, the program is based on math. I always recommend math as the “starting point” for careers in data science or statistics: it’s a common foundation so we can communicate ideas precisely and in a common language. I learned to code in R mostly by doing, and I became fluent in visual tools such as Tableau at work. I have a relatively weaker background in computer science, although many of the tools are the same (such as numerical methods).
Two, I was looking for a program that would fit my full-time work schedule, and the evening class hours were perfect for me.
Three, Georgetown has an excellent overall academic reputation, and the department and its faculty certainly live up to its name. It was an honor to be a student there.
How did the MAST program prepare you for your career?
Certainly, I had more job offers once I earned the MS degree, both within and outside of my organization. I wasn’t usually even looking for a job, but people in my network would hear about me. Within Georgetown, I also made lifelong friends and colleagues (both students and professors) and the alumni network is great for finding career and research opportunities.
Certainly, having gone to Georgetown helped me in pursuing a Ph.D. Most importantly, the professors were like family. I felt like my professors cared about me and they helped me catch up when my mathematical background was lacking.
What’s your advice for alums?
One, don’t be shy about re-learning old material. I often forget concepts and I have to review old notes (luckily I save everything, I’m very organized with my notes and assignments). It’s a good idea to review basics such as probability theory. I notice that the greatest NBA players are always practicing lay-ups on their own time (Ray Allen is one example), and the same idea applies to us all: we need to keep our foundations strong.
Two, help others. Learning math and statistics is a process. Other people will appreciate it so much if you share your knowledge and advice. Never pre-judge anyone as being “not smart”—that person was me! We all learn through persistence and practice, so I try to be genuine with those who were once in my shoes.