51–60 of 143 posts
" Brainteasers are popular." Can somebody explain why? I'm genuinely interested, since in software dev interviews we see less and less brainteasers...
There's smart quants/programmers/traders/etc and people good at solving brainteasers. You need the former, but people who use brainteasers in the interview process typically only find the small sliver of overlap in the venn diagram.
"Hirers tend to be looking for strong programming and data management skills as much as mathematical ability" This cannot be over emphasized. You cannot just have an aptitude for math/statistics/probability. You will need to be able to write high performance production quality code.
95% of quant code is not only fails to achieve low-latency, it's utterly incorrect, unmaintainable and frankly deranged.
" Brainteasers are popular." Can somebody explain why? I'm genuinely interested, since in software dev interviews we see less and less brainteasers...
Interviewers are (should) not interested in the correct answer. They are interested in seeing the candidate think out loud. They want to see how you start tackling difficult problems.
Earlier quoted context omitted.
Now if you are a programmer, how do you get into the industry? You need to know stats, machine learning, and programming, really well. My experience being a quant and being around quants is that, sadly, they don't get to use much machine learning. Some do, but it seems like 97-98% of the work is much more mundane. Am I correct in perceiving that many hiring managers expect more in the way of interesting experience th…
> Am I correct in perceiving that many hiring managers expect more in the way of interesting experience than they really need (or have to offer in the work that needs done)? Yes I think you are Michael. Especially for quant jobs 2 and 3 that I enumerated. The only reason I listed machine learning is that it is the trogan horse that gets programmers to learning stats. I can't over emphasize enough just how much you ne…
Ah, that makes perfect sense.
To me, the appeal of machine learning is that it's challenging and respected enough that programmers doing it get the autonomy to work in any subfield of computer science, from the high level to the low. If you're a data scientist and you say you want to use Clojure or Haskell (a high-level concern) or that you want to do GPU programming or dive deep on assembly (low-level work) you can. Machine learning, 10 years ago, was extremely appealing because software managers were figuring out that they needed it, but most admitted they knew little about it, so they gave a lot of autonomy to individual contributors. (That may change, and "data science" may become thoroughly commoditized.)
It's the Fundamental Theorem of Employment: you're usually hired either to do (a) something your boss can't do for himself or (b) something he doesn't want to do. With (a) you get respect and autonomy and high pay; with (b) you get treated like a commodity. "Data scientist" (or software "architect" vs. "engineer") is, often, a programmer who's managed to learn enough of "the hard stuff" to move himself over to (a). It's the (b) category of engineers who get stuck on "Scrum teams".
I feel like some of the crowding of "data science" (and, as you noted, not all of the "data scientists" know what they're talking about) comes from the way that "Agile"-style micromanagement has made the rest of programming so braindead. There are people like me who enjoy the hard mathematical aspect, but others who've just learned that if they call themselves "data scientists" they get more interesting projects and don't have to munch on Scrum tickets. For them, the math is an impediment rather than a challenge and an attraction.
I mentioned the deep dive into the machine learning techniques as unfortunately most of the programmers I meet who call themselves data scientists, just aren't very good at stats.
There's a depth vs. breadth problem, because machine learning is a much, much bigger field than many people think. I've gone pretty deep on penalized regressions (e.g. ridge and Lasso with large numbers of features) but know only the basics about tree-based models. I can read the papers on neural network architectures (e.g. convolutional nets) and implement them, and I understand the theory that led to them, but I still lack some of the intuition (like why rectified linear units are more useful in image processing than regular logistics).
I feel like there are some people who pick up a lot of vocabulary and interview well (but not with you, because you actually know the field) but are really just playing around with parameters. I like the math, but "data science" is mostly bullshit and I hope the term will die; I want to see more machine learning and less business pomposity.
Interesting! Thanks a lot for writing/posting this. I enjoyed the read. One thing I had to chuckle at was this: >Mark Joshi obtained a B.A. in mathematics (top of year) from the University of Oxford in 1990, and a Ph.D. in pure mathematics from the Massachusetts Institute of Technology in 1994. He was an assistant lecturer in the department of pure mathematics and mathematical statistics at Cambridge University from…
From the stories I've heard the industry stint was purely about money. He had grown up very poor and had four children, a few years working on oil field and bond models was enough to supplement his professor salary for the rest of his career.
On the other extreme is someone like Simons who left pure math to found a hedge fund and is still at it billions of dollars later.
"Hirers tend to be looking for strong programming and data management skills as much as mathematical ability" This cannot be over emphasized. You cannot just have an aptitude for math/statistics/probability. You will need to be able to write high performance production quality code.
95% of quant code is not only fails to achieve low-latency, it's utterly incorrect, unmaintainable and frankly deranged.
I got an applied math Ph.D. with a good course in measure theory and stochastic processes from a star student of E. Cinlar and a lot in optimization, wrote my dissertation on stochastic optimal control, had a solid background in software, especially in scientific computing, including a lot of applied statistics, sent a resume to Fisher Black at Goldman Sachs, and still have his nice answer back that he saw no opportu…
"Hirers tend to be looking for strong programming and data management skills as much as mathematical ability" This cannot be over emphasized. You cannot just have an aptitude for math/statistics/probability. You will need to be able to write high performance production quality code.
Now admittedly this is sampling bias, but I have never seen code written by a quant that could be put into production without being reworked by a software professional. The skill sets are in some way exclusive of each other. Quants do lots of exploratory work. This involves tons of one-of experiments and throwing away things that don't work. They often frequently don't have the operational understanding of the way th…