The field of financial engineering is fascinating. More fascinating is the range of creative talent I have met for the last 14 years as executive director of the Master of Financial Engineering Program at the Haas School of Business. Among those who recruit our students and alumni, and among our own students and alumni.
The Berkeley Admissions Team has a knack for bringing on board the best of the best. Often we read some of what our detractors say: that we are on the West Coast so we don’t specialize in placing our students on the East Coast. Wrong. We place more students on Wall Street than any other MFE Program in the country. Our database is impressive: thousands of deep connections at all levels within the finance industry. And we place our students and alumni one by one.
While I personally continue to serve Wall Street and other financial hubs in the world by helping place alumni and current graduates in key firms, I have been excited to see how opportunities for financial engineers are quite vast. More than 90 % of the students in our MFE Program will continue to secure positions as structurers, traders, trading strategists, portfolio managers, researchers, etc… but recently, I have seen how startups are interested in our students and vice-versa; for instance an MFE13 graduate has just rejected a structuring position at a top tier investment bank in Asia and accepted a job as data scientist at a small but growing startup company located in Mountain View. Two other students followed suit and accepted full time positions at that firm which offered a very high compensation and equity in the firm. The company was founded with the vision of developing analytical solutions that help businesses make better decisions through objective analysis of data. The graduates’ initial responsibilities will be focused on building models and methodologies for the core analytics engine. This is similar to how quants at major investment banks build pricing and risk models, but there are some important differences. First, there are very unique challenges associated with corporate data that do not exist – at least in the same magnitude - in developing models for traded instruments. Quants at a typical investment bank often know what data is available, how it can be used, and the methodologies that need to be applied. Our MFE 13s’ task is much harder because corporate data is fragmented, in proprietary formats, and it is much harder to ascertain which pieces are important and how to process them to gain useful insight. Second, the methodologies and framework for modeling traded instruments are fairly mature and many jobs in this field are dedicated to the application of the established principles. For my former students, the ground is much more pristine. No one has really attempted to approach decision-making via a systematic generic framework and there are no established tools or best practices to tackle this problem other than expensive bespoke solutions that only fit specific situations. This puts the company and the MFE graduates in a unique position to innovate and solve a problem that has never been solved properly. For instance, these MFE13 graduates’ roles in this growing startup are quite fluid; they are expected to contribute and lead initiatives in other areas than just their initial role as data scientists. The founders of the company subscribe to the philosophy that the best path to success is to encourage employees to step up to the plate and take responsibility for anything required to deliver on the shared vision of the company. This requires the successful candidate to take initiative to identify and overcome obstacles in a wide range of areas including product development, business planning, sales, client on-boarding, etc. The wonderful thing about this opportunity is that the alumni will get to work with and learn from a very talented, experienced, and entrepreneurial team which has the shared goal of building a worthwhile product.
In conclusion, some words of wisdom: consider all of the offers/options that come your way.
So long,
Linda