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It was the first day of the Masters in Financial Engineering (MFE) at UC Berkeley’s Haas School of Business, and Bear Stearns was selling for $2 a share. Because the programme starts in March, my new classmates and I received an immediate object lesson in financial meltdown. Moreover, the demise of one of the credit crunch’s most noteworthy victims was being blamed, in part, on some of the very products we would soon be learning to construct and price.

When chatting with someone from the previous year’s class, they described the number of hours students spend together, working on the many hands-on projects, as “like being on a submarine”. I remember hoping it wouldn’t sink.

The only good thing you could say for the Bear Stearns collapse was that it made for an excellent ice-breaker, as this group of about 60 strangers from more than 20 countries, physicists and actuaries, former traders and a quantitative biologist or two milled around the coffee and tea urns, excitedly discussing whether JPMorgan would be forced to raise its bid. When a well-dressed guy, who I thought looked very “Wall Street”, made a passing reference to an Isaac Asimov science fiction novel, where someone presses a few buttons on a calculator and predicts the fall of galactic civilisation, I knew I had come to the right place.

Less than six months previously, I had been sitting in an internet cafe in Shanghai, reading ominous market news, and thinking I had definitely made the right decision to take a gap year, rather than go into the “quant” job market straight from college. Up until a year before I graduated from Stanford, I had planned to pursue a PhD in mathematics, without giving any thought to how I would apply my degree in the real world. However, when I spoke to several friends who had opted for industry over “ivory tower”, I realised that quantitative finance was a much better fit for my interests in applied maths and programming. I decided I wanted to do a Masters embedded in a business school rather than in a mathematics or statistics department.

While I was waiting to hear from university admissions offices, I travelled through China, eventually rented an apartment in Shanghai and took daily Mandarin lessons, hoping to create another part of the foundation for my would-be career in a global industry. When I received news that I had been admitted to the Berkeley MFE, I immediately booked an airline ticket to California.

The programme maintains online profiles of current students. I remember reading these as I considered applying and being amazed that one discipline could encompass the expertise of so many varying backgrounds. By now, I have completed the first quarter. What strikes me when I chat with my classmates is not that we all found it quite challenging, but that we cannot agree on which parts were most difficult. The former engineers were able to breeze through programming econometric models in the empirical finance class, but stumbled when asked for the economic intuition behind the model. Those students with previous trading experience could show the scientists how to watch the market, but often needed a hand with the theorems behind stochastic calculus. For me, the most frustrating experience was sitting in front of the Bloomberg terminal in the computer lab, thinking: “I can solve stochastic differential equations, why can’t I get this funny-looking computer to work?”

Another challenge, especially for those of us from more sedate academic backgrounds, was preparing for the intentionally bruising style of Wall Street job interviews as we search for our three-month internships. From mock interviews conducted by MFE alumni in the first week of orientation, to resumé workshops, to the weekly practitioner seminars, the non-academic learning curve has been as steep as, or steeper than, the academic one. At the time of writing, I have recently accepted an offer of an internship with Goldman Sachs in London, after a gruelling interview process I would not have survived several months ago.

Three months after Bears Stearns, I am struck that financial engineering seems surprisingly resilient for a relatively new field. The job market is tougher than it was, but firms still come from all over the world to recruit newly minted financial engineers. And so clever, hungry minds will continue to be drawn to this type of degree to launch or advance their careers. Whenever I attend a barbecue thrown by a friend who is now doing a maths PhD, or a house party with someone in the physics department, there is always some curious graduate student who approaches me and asks, “so, what is financial engineering, anyway?” I try to explain that, as finance has become more quantitative and complex, there is an arms race in pricing and managing risk. “That’s interesting, and you say they hire math people? Do you know if your programme is still accepting applications?” The submarine is still afloat.

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