61–70 of 143 posts
"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…
I'd like to see an article titled "Why Get a Quant Job in Finance?"
Because you get to work on really interesting math problems, in an environment that isn't stingy with pay or equipment and has clear objective measures of what things are important for the business?
Other ML fields like robotics and deep learning have virtually limitless applications that may benefit society in some way. Of course, I have heard the arguments for statistical arbitrage being useful by way of reducing inefficiencies in the market and providing liquidity, but this is far less tangible.
Earlier quoted context omitted.
I work as a quant and I like to give brainteasers in interview. It definitely does not tell me if someone will necessarily be good at his job or a good fit for the team, but it does help me rule out a lot of potential candidates. These are not very tricky brainteasers that depend on getting a particular insight. They're actually somewhat pedestrian. However, if you can't manage to competently work out a simple, well…
Can you give an example or two of the more complex issues you deal with? I honestly have no idea.
chollida1's comment is spot on and I oscillate between 1 and 2.
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…
But to your point, the CTO of the fund was a pure math phd who got into finance/coding when his advisors asked "Ok so now how are you going to make money". which is common for most of the heavy duty quants I know.
Having hired a lot of quants and programmers in finance I can probably give a bit of background on what to expect: Get this book, read it and understand it. http://www.amazon.com/Heard-Street-Quantitative-Questions-In... Quants tend to be in 1 of 3 categories: 1) pricing quants, you work for a bank or investment house like Goldamn. You know stochastic calculus very well, you know finite differencing like the back of…
My other two options are working at an ambitious new robotics startup, and a PhD in deep learning.
I have a few questions:
1) What would you say is a reasonable salary range for someone with a master's degree in computer engineering and a year of experience in back office, as well as an assortment of ML side projects? How high could you expect it to be in 2, 5, 10 years?
2) Is it very difficult to break into the industry? This opportunity just landed in my lap (recruiter), and I'd like to know how likely it is that I'll find something like it again.
3) Will a PhD in machine learning (and the resulting five year gap in the industry) make me more or less employable? How will it affect my salary/job opportunities?
4) Just how much of the job is reading and implementing machine learning papers, and how much of it is general software engineering?
5) Where can you derive meaning and satisfaction from a job in quantitative finance? How do you reconcile the opportunity cost to society from not working on directly socially beneficial applications in fields like medicine and artificial intelligence?
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How would one go about getting a job as a #3 with an MS in economics but no practical finance experience? I can code reasonably well, know stats reasonably well, and know calculus reasonably well, but could easily bone up on all 3 in about 2-3 months, enough so to confidently pass the interview process. But how would I get an interview without any finance connections?
People act like finance is some secret inner sanctum, but it isn't. Finance companies/hedge funds advertise on job boards like any other company. They also often use very aggressive recruiters so finding the recruiting company du jour in your area and talking to them can help.
I've worked with some brilliant mathematical people, and I've worked with many who were not so brilliant. There are a ton of people who do the 'math' and 'stat' work at major financial institutions who are nowhere near as good at math as they should be. I haven't found it to be much different that any other industry from a programmatic perspective.
Having hired a lot of quants and programmers in finance I can probably give a bit of background on what to expect: Get this book, read it and understand it. http://www.amazon.com/Heard-Street-Quantitative-Questions-In... Quants tend to be in 1 of 3 categories: 1) pricing quants, you work for a bank or investment house like Goldamn. You know stochastic calculus very well, you know finite differencing like the back of…
I would have thought no exceptions. What are the characteristics of the few who make good traders despite not being exceptional at mental math?
Having hired a lot of quants and programmers in finance I can probably give a bit of background on what to expect: Get this book, read it and understand it. http://www.amazon.com/Heard-Street-Quantitative-Questions-In... Quants tend to be in 1 of 3 categories: 1) pricing quants, you work for a bank or investment house like Goldamn. You know stochastic calculus very well, you know finite differencing like the back of…
" Brainteasers are popular." Can somebody explain why? I'm genuinely interested, since in software dev interviews we see less and less brainteasers...
In my experience in finance it's all about the interviewer showing the interviewee how smart she is. 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.
If you're interviewing someone and you're lazy enough (or not confident enough) in your skills at assessing a candidate, that's a weakness.