Quant Hedge Fund D.E. Shaw Fires 150; 10% of staff
dealbook.blogs.nytimes.com
dealbook.blogs.nytimes.com
I once had a very challenging technical job interview there. Several interviewers had PhD in Computer Science.
IIRC, even the HR person I spoke to had a PhD (russian literature, I think).
They are a very impressive firm, and I hope they recover from their recent misfortunes.
DE Shaw & Company takes its very smartest people (and a non-trivial portion of their profits) and puts them to work at DE Shaw Research, where they work on protein folding and other computational biology that could help cure cancer and HIV. I think that's a pretty worthwhile use of talent :-D
From what I was told, David Shaw is spending most of his time on the research group these days.
They ostensibly don't consider themselves a finance firm - they consider themselves a tech firm that will use their skills wherever they can to increase efficiency and make money.
I doubt it is fair to say they are ignoring software improvements. I saw Shaw talk about the machine architecture and he had a lot to say about balancing programmability for later software improvements vs. specialized computational resources. Also, it was his algorithmic improvements, the "neutral territory" methods, that were inspiration for the machine. Are NT methods still state of the art for molecular simulation?
Anton is impressive in that it can provide millisecond long trajectories of protein/solvent systems, but single trajectories are of limited utility. You need many (1000s) of such trajectories for statistical analysis of the molecular system. Further, there already exist several clever methods that leverage chemical statistical mechanics to provide the same analysis without the need for single long trajectories. As an example, see Pande’s work developing Markov models of protein folding using millions of short trajectories between metastable states. This method has already provided a complete statistical analysis of protein folding for proteins that fold on times scales an order of magnitude beyond what Anton can simulate.
As for Desmond, there already exists a plethora of free MD programs (MMTK, LAMMPS, NAMD, CHARMM, Gromacs, and many more). Many of these, especially Gromacs, have already been highly optimized for a range of hardware and I wouldn’t expect Desmond to surpass these free codes by a margin worth dropping dollars.
Personally, I still have high hopes for DE Shaw Research, I just don’t see how their current offerings will turn a significant profit or greatly advance science. I’d love to be proven wrong, and I’m sure they’ll have plenty of additional novel future projects, some of which could be paradigm-shift-changing for chemical physics and molecular biology. My guess is that such advances won’t come from their hardware geniuses, but instead from their math/physics geniuses that will develop new statistical mechanics methods to bend & contract in silico time.
my coworkers at morgan were all physics or cs phds. and a consulting expert got a nobel prize while i was there. amusingly, i interviewed at google around the same time, and some of the engineers were condescending about the quality and education of my coworkers.
What's sad is that these smart people, including ones I've met while working as a SysAdmin for a Prop Trading firm often don't have access to the capital to start their own shops. This is due to the incestuous nature of the Finance world, where it's a lot more about who you know than what you know.
Sadly, they're often in the employ of third-rate CEOs, who always get a cut off the top, and pay the producers a mere fraction of what they made for the firm and their clients.
The Medallion Fund has its own internal trading desk, staffed by approximately 20 traders, and trades from Monday opening bell in Australia through Friday closing bell in the US.
FWIW, in my experience the handful of citadel programmers I've interacted with aren't quite in the same league as shaw/rentech/getco/jane st/etc. I have no idea how that reflects on their returns though ;-)
They show off to attract similar candidates because they know they are nowhere compared to these biggies.
If I have a offer from Goldman why the fuck I join de shaw or xyz company.
I would not invest in anything from them however. Medallion has been closed for new money for many years now and all other offerings performed poorly to say it politely.
No degree.
But I was laughing a couple months later when I was working across the hall at Akamai ...
This is almost a pure software business, so why aren't these companies more like startups? Is it because of the expense of getting fine grained and low latency market data [eg. exchange colocation]? Or is it due to the contacts needed in the biz to get the 'investment' funds to trade with? Or is it because only large financial entities have access to these risky speculative trades?
Side note : I see Jane Street Capital use Ocaml, and some other quants use KDB/Q for implementing these kinds of algorithms, so it seems innovative languages give leverage here. I was thinking Node.js + a js BTree api to access streaming data would be a nice dev environment.
A trading firm needs above 2 items in addition to technical skills to succeed. A team of good people with all 3 above items (capital, trading, tech) have a good chance to succeed.
In fact, Citadel (one of the largest quant hedge funds) was started by one person (Ken Griffin) when he was a undergraduate from a Harvard dormitory. It is pretty much a startup success story.
There is a major culture difference: trading is the key activity; coding is only secondary. This may explain why trading firms usually have a typical wall-street tough culture, and don't feel like a typical silicon-valley startup.
There are. http://www.forbes.com/2010/07/28/high-frequency-trading-pers...
Incidentally Jane Street isn't a hedge fund, it's a proprietary trading firm.
ps. I never really understood the difference - Hedge Funds seem to be more about speculating via leverage than 'hedging'? [using their own proprietary algorithms to do that]
Market changes constantly. Many trading algorithms are essentially market-data driven and need to adapt to the market or lose.
If it's the bottom 10%, it should actually improve company's performance.
I personally think it's terrible management practice, but it seems to work for them, and there's always fresh young blood coming in at the bottom.
http://en.wikipedia.org/wiki/Jack_Welch#Tenure_as_CEO_of_GE
http://www.geek.com/articles/chips/updated-rumors-intel-layo...
(The Intel practice was also confirmed in Andy Grove's book "Only the paranoid survive".)
> Intel announced it will reduce its workforce by 4,000 workers (5%), mostly through attrition or voluntary separation programs.
I joined Intel shortly after that article was written (2002). For much of my tenure (3 years), Intel had a US hiring freeze (except through acquisition, how I got hired) and aimed to reduce its headcount primary through attrition, not layoffs. I felt like Intel treated its employees pretty well.
In any case, I think Andy Grove's book is a better citation, but some people on the Internet don't read books.
I think the key (and I don't have a lot of experience with folks who work at GE, so I can't speak to it's validity) is that it works if deployed as part of an overall system.
That system is that you reward the top performing earners heavilly (with bonuses and promotions), and remove the bottom 10% annually. In theory, people are constantly being kept abreast of their performance, and given opportunities to improve; so that those in the bottom 10% shouldn't be surprised when the end of the year comes and they are laid off.
Welch defends the practice by talking about the alternate scenario where people are kept on staff for years who aren't performing well, have never been told that is the case, and are suddenly surprised when one day the company needs to have layoffs and are terminated. He calls it "false kindness".
I've always like the part of Differentiation where you let people know where they stand. I've thought it would be nice to work somewhere that had a better model for performance reviews (where I work presently, they are basically a checkbox).
I'd love to hear from someone at GE as to whether the system actually works the way Jack Welch describes it.
Now, I agree that quantifying that is non-trivial. I've always been interested in places where they actually do that (and wonder how accurate it is), but I wouldn't be surprised to find that people who find themselves in that lowest 10% don't find themselves there on a whim.