From physics to Goldman to Y Combinator

Better to die free than live as a slave, or something like that. The author describes his path from physics grad school to Goldman to a bay area startup. Via maoxian.

Why founding a three-person startup with zero revenue is better than working for Goldman Sachs

I joined Goldman Sachs in 2005, after five flailing years in a physics Ph.D. program at Berkeley.

The average salary at Goldman Sachs in 2005 was $521,000, and that’s counting each and every trader, salesperson, investment banker, secretary, mail boy, shoe shine, and window cleaner on the payroll. In 2006, it was more like $633,000.

In the summer of 2005, I took one look at my offer letter and the Goldman Sachs logo above it, another look at my sordid grad student pad, and I got on a plane to New York within the week. I packed my copy of Liar’s Poker for reference.

My job on arrival? I was a pricing quant on the Goldman Sachs corporate credit trading desk1. We traded credit-default swaps, both distressed and investment-grade credit, and in the bizarre trading experiment assigned to me, the equity part of the corporate capital structure as well.

There were other characters in this drama. The sales guys were complete tools, with a total IQ, summing over all of them, still safely in the double digits. The traders were crafty and quick-witted, but technically unsophisticated and with the attention span of an ADHD kid hopped up on meth and Jolly Ranchers. And the quants (strategists in Goldman speak)? Mostly failed scientists (like me) who had sold out to the man and suddenly found themselves, after making it through two years of graduate quantum mechanics, with a bat-wielding gorilla peering over their shoulder (that would be the trader) asking them where their risk report was.

... The sad truth is: quants were the eunuchs at the orgy. We were the ever-present British guy in every Hollywood WWII film: there to add a touch of class and exotic sophistication, but not really matter much to the plot (and maybe even conveniently take some bad guy’s bullet).

But things weren’t all bad! At its best, when the markets presented an apocalyptic Boschian landscape of damned souls torn asunder by hellish tortures, every Goldman grunt, sergeant, or general would close ranks and form a Greek phalanx of greed. Unlike almost every other bank on the street, Goldman could actually calculate its risk across desks and asset classes, out to five decimals [see footnote].

... What’s work like now? Writing code. Worrying about everything from our credit card billing to the pile of dirty dishes in the sink that will give us all diptheria some day. Writing linkbait blog posts to get us free PR (like the one you’re reading now). Schmoozing with investors, and playing the junior high school popularity contest that is startup funding. Keeping jealous tabs on other startups to see how they’re doing compared to us. Trying to put myself in the mind of our users to make something they’d want. Oh, and launching…finally, good God…launching.

You see, starting a product from an empty text buffer is very different from keeping a well-oiled money-machine running8. I’ve had apocalyptic fights with the other founders that almost ended in fisticuffs. I’m watching my four-month-old daughter grow up via Skype. These jeans I’m wearing will likely fuse with my skin at some point if I don’t take them off. I haven’t seen a paycheck or a loving woman in much too long.

You know what I regret most though, going from Goldman to this?

Not having made the switch earlier. ...


[Some footnotes]

It’s a somewhat different story on structured credit desks, where numerical modeling is perceived to be more important. Also, on algorithmic trading desks, where the quant writes the code that does the trading, and the sometimes blurry line between quant and trader basically disappear.

At the risk of getting sued, let me throw you geeks a bone and part the Goldman veil a bit. The Goldman Sachs risk system is called SecDB (securities database), and everything at Goldman that matters is run out of it. The GUI itself looks like a settings screen from DOS 3.0, but no one cares about UI cosmetics on the Street. The language itself was called SLANG (securities language) and was a Python/Perl like thing, with OOP and the ORM layer baked in. Database replication was near-instant, and pushing to production was two keystrokes. You pushed, and London and Tokyo saw the change as fast as your neighbor on the desk did (and yes, if you fucked things up, you got 4AM phone calls from some British dude telling you to fix it). Regtests ran nightly, and no one could trade a model without thorough testing (that might sound like standard practice, but you have no idea how primitive the development culture is on the Street). The whole thing was so good, I didn’t even know what an ORM really was until I started using Rails and had to wrestle with ActiveRecord. The codebase was roughly 15MM lines when I left, and growing. I suspect my retinas are still scarred by the weird color blue SecDB was by default.

If doing a startup is like rolling a boulder up a hill, then working at Goldman Sachs is like rolling it down the hill: you just have to stay out of the way of the boulder.