Succeeding on a traditional finance path
Before applying, I had thought a lot about how an MFE would help me leverage my experience and skills in the job market and I had a very clear picture of what I wanted to do when I applied.
The Berkeley Haas MFE offers two paths: one toward machine learning applications, the other, more traditional, is toward derivatives pricing. With my background in structured products, I knew I would take the second path.
My MFE studies helped me think more deeply about the models we use in risk managing the portfolio of bespoke products we create for clients.
Likening trading derivatives to playing poker
Poker and trading are both imperfect information games. In both, you need to not only consider the expectancy but also the variance of outcomes.
The first step is to separate the quality of your outcomes from the quality of your decisions.
Having the right mindset is essential. You learn to make decisions based on probabilistic models.
Banking on deep industry connections
The Berkeley Haas MFE program is number-one in its connectivity to the industry.
The program office, the curriculum, and the faculty share a real emphasis on employability.
The fall internship is unique to the Berkely Haas MFE. The timing means you are prepared to contribute and positioned to get a lot of learning from the experience.
Competing from a position of strength
Between competitions and opportunities to work with professors and investment firms on industry projects, Berkeley Haas offers the most extra-curricular opportunities of any MFE program.
The collaboration among my diverse teammates in the Citadel Data Open competition encouraged us to learn from each other. We had an applied mathematician, a statistician and an economist, everyone under the STEM umbrella.
With a diverse team, you learn how to get the best out of each person, for each of you to play to their strength. Our strength showed: out of 6,000 participants and 20 teams in the final event, we won the competition.