Finding commonalities while changing career direction
A career in data science was not at all what I expected when I applied to the Berkeley Haas MFE program. I enjoyed the serious forecasting I did at the central bank of Colombia, looking at inflation, macroeconomics, and contributing to important policy decisions. My work at Facebook is just as challenging and has important ramifications as well.
Surprisingly, there are lot of similarities between the cultures at the central bank of Colombia and Facebook. They both promote teamwork and collaboration, and both cultures demand intense concentration to answer big questions.
Working in quantitative strategy in the financial sector has a lot in common with being a data scientist. The tools and theories I use are the same. What has changed is the type of problems I am asked to solve.
Tailoring your coursework for practical impact
In the Berkeley MFE program, you have a lot of flexibility to tailor your studies. The Program Office really supported my determination to complete two more projects in addition to the Applied Finance Project that all MFE students do. The attitude seems to be, if you want it, we will help you.
One of my projects, focused on machine learning. For the other, I worked with Professor Martin Lettau on empirical analysis. Both were challenging and grounded in the real world.
Thanks to my project work, I had an actual model I had developed to the interviewers when I pursued my internship with Uber. Being able to demonstrate my abilities in such a concrete way, when I had no previous experience in data science, was very important in landing that internship.