SERVING THE QUANTITATIVE FINANCE COMMUNITY

 
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volasmiler
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Interview with big Quant hedge funds

March 26th, 2014, 4:03 pm

Hi,Does anyone here have experience with interviews at the big quant HFs, such as AHL, Bluecrest or Winton?What kind of questions do they tend to ask? How should I prepare?So far, I only went through IB derivative pricing interviews, but since the requirements for buy-side jobs are quite different I assume that the interviews will also differ (even though the brainteaser part might be similar, I suspect).Any help is much appreciated!Thanks!
 
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neuroguy
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March 27th, 2014, 9:38 am

QuoteOriginally posted by: volasmilerHi,Does anyone here have experience with interviews at the big quant HFs, such as AHL, Bluecrest or Winton?What kind of questions do they tend to ask? How should I prepare?So far, I only went through IB derivative pricing interviews, but since the requirements for buy-side jobs are quite different I assume that the interviews will also differ (even though the brainteaser part might be similar, I suspect).Any help is much appreciated!Thanks!Statistics... very important, particularly complete fluency with basic concepts.Algorithms... as a rule more important than any specific (low level) language (unless you are applying for a developer role). Ability to think creatively and generate novel ideas... and how you have demonstrated this in the past. (I was once asked to give a novel solution to a famous machine learning problem with an already well known solution.)Brainteasers are far less prevalent. Normally the questions are around your specific proficiencies and they get pretty deep. Normally, buy-siders could not care less if you can estimate the number of bananas imported to Sweeden. Rather they want to know that you are going to generate value as soon as you hit the ground and that you are going to fit with the research process. If you say something on your CV, you should anticipate very detailed questions in that area.Winton also has written statistics puzzle test.Best of luck.
Last edited by neuroguy on March 26th, 2014, 11:00 pm, edited 1 time in total.
 
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roundandround
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April 16th, 2014, 12:40 pm

Any idea about the process at G-Research? I am a soon to graduate Computer Science PhD (focus on Machine Learning) from the US. What are the exit options at places like Winton and G-Research, if someone were to join them as a quant researcher?As a sidenote, how much do these two pay (base+bonus) for quant research roles? I looked around the internet and base salary seemed fairly low (~60k pounds).
 
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neuroguy
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April 17th, 2014, 8:42 am

QuoteOriginally posted by: roundandroundAny idea about the process at G-Research? I am a soon to graduate Computer Science PhD (focus on Machine Learning) from the US. What are the exit options at places like Winton and G-Research, if someone were to join them as a quant researcher?As a sidenote, how much do these two pay (base+bonus) for quant research roles? I looked around the internet and base salary seemed fairly low (~60k pounds).G research has a pretty thourough written math test. I have also heard that the interviews are hard, again they focus on statistics. I have never interviewed there however. Try searching these forums since I am pretty sure it has been discussed before.If you want to know 'exit options' then you could search linkedin for ex-employees of your target firm. However it is my observation that once you work at one of these places you get quite type-cast, so either you go and work for another similar fund or you set your own thing up. Just a tip: dont focus too much on your 'exit'. Its not quite like the game of musical chairs that the sell-side is. Typically you will enter from PhD in a state where you will have to learn a lot. The last thing funds want is to teach eveything about their processes (which takes quite a time since they tend to be idiosyncratic by design) just for you to turn around and 'exit'. For a fresh PhD with no experience base + bonus could very roughly fall within the range £80k - £150 in London. Often on the buy side the rule of thumb for junior roles is that all else being equal (and if you are good and the performance of the fund is good) then you are looking at total comp of 2xbase (base plus bonus around size of base). Not sure that I would say a base of £60k for a starting role is 'low'. You do realise that this kind of money is the terminal salary for many, many jobs right? Typically base will be a bit lower on the buy side than on the sell side, but bonus tends to be a bit bigger (again, as long as business is good). To get more information about comp, look at the surveys done by the CFA (out of date ones can be found on line, or you can purchase a current one I think) and there is also a fairly comprehensive survey done by Wall St Oasis (which is a useful resource if can tolerate the psychopaths who frequent it) but again you have to pay a small fee to get hold of it. Also a good (I emphasize 'good') head hunter should be able to give you advice about your market value.
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roundandround
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April 18th, 2014, 4:06 pm

Great answer, thanks. Which specific areas of Math and Stats should one brush up for such interviews? Any particular books/references? Is any of them into asking puzzles? I am targeting Quant focused openings in UK and rest of Europe (I am not an EU citizen). Which other places could I look at? Other than these two, I have Bluecrest, Optiver (Netherlands) and Jump Trading on my list that could be interested in someone with an ML PhD. And what about other places in Asia (Hong Kong and Singapore)? I heard Citadel has offices in HK, and Jump Trading in Singapore. Any other possibilities? So let's say that £60k is indeed the starting base salary. How does this number change over the years? Just out of curiosity, if I were to work at a text analytics startup (relatively unknown, not funded by "leading" investors, but still very well funded, and doing good work) say for about a year, and then apply to these places, would I be any more/less attractive to them?
 
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farmer
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April 18th, 2014, 10:02 pm

QuoteOriginally posted by: roundandroundJust out of curiosity, if I were to work at a text analytics startup (relatively unknown, not funded by "leading" investors, but still very well funded, and doing good work) say for about a year, and then apply to these places, would I be any more/less attractive to them?Now you are asking for more wisdom than any man has. I think text analytics is the new frontier. If this is at all interesting to you, you should do it. Maybe a year from now, people at Winton will wonder how they can jump out of their mature industry into this hot field, or apply its new tools.People talk about the complexity of life, cells, and the human genome. Language is less awesome, but still on the same scale. It has crazy compression properties.If anything, text analytics is too ambitious. How many decades before a computer will be able to penetrate what I have just said? Maybe it only has to be faster or cheaper in limited cases.
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roundandround
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April 19th, 2014, 4:39 am

My question was more to do with the perception. How would these hedge funds view someone who is working at a startup funded by tier 2 investors? I know that some of the places can be obsessed with brand names, and hence the question. Most people in NLP and text analytics are in fact focused on solving domain specific and niche problems, and this is where text analytics really shines. There were several attempts made in the community to get people to focus on fundamental problems along the lines you mention (conferences like DUC, MUC, ...), but that effort fizzled out long ago. These days, it is mostly about solving specific problems. Personalizing education (Knewton, Coursera), Q&A Systems (IBM's Watson), etc. are all quite big. And then of course, there's this emerging trend of marrying text analytics with other disciplines (think Visual Analytics). All in all, a very good time to be in this space.
 
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neuroguy
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April 20th, 2014, 8:13 am

QuoteOriginally posted by: roundandroundGreat answer, thanks. Which specific areas of Math and Stats should one brush up for such interviews? Any particular books/references? Is any of them into asking puzzles? I am targeting Quant focused openings in UK and rest of Europe (I am not an EU citizen). Which other places could I look at? Other than these two, I have Bluecrest, Optiver (Netherlands) and Jump Trading on my list that could be interested in someone with an ML PhD. And what about other places in Asia (Hong Kong and Singapore)? I heard Citadel has offices in HK, and Jump Trading in Singapore. Any other possibilities? So let's say that £60k is indeed the starting base salary. How does this number change over the years? Just out of curiosity, if I were to work at a text analytics startup (relatively unknown, not funded by "leading" investors, but still very well funded, and doing good work) say for about a year, and then apply to these places, would I be any more/less attractive to them?Personally I think the potential of text analytics is a bit over stated in the financial sphere. Some of the tools used to achieve it however are possibly of general utility. It might make you attractive to them from the point of view that it demonstrates some experience of real world commercial applications. It might also help your coding/system design skills. But I don't think it is something that would be a massive selling point in and of itself. If you want to do finance, I would just set about doing it now.Other possible firms: Aspect capital, D.E. Shaw, Dimensional fund advisers, Man AHL, Cantab capital, Transtrend, Sun trading.Regarding subject matter... what ever you have said you know about + the basics that you 'ought to know'. You kind of just have to figure that out for yourself... sorry. Regarding 'puzzles' I have actually never been asked a straight brainteaser in any interview I have ever been in. I have always been asked to solve things that are related to finance in some way, or to the role I was looking at. I have been asked things like simple stats proofs, markov chains, recurrence relations, portfolio optimisation etc... Just my experience though.
 
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roundandround
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April 20th, 2014, 11:26 am

Great tips, thanks.One final question. Are any of these firms overly picky about the ranking of the university one gets their PhD from? I am from a US university ranked ~30 for Computer Science. I do have good publications in theoretical and applied ML though. Looking through LinkedIn profiles of Quants at these places, I mostly only see people coming in from big name schools and/or with significant prior experience.
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BudFox2013
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Interview with big Quant hedge funds

April 26th, 2014, 9:11 pm

I did three rounds of interviews at Jump Trading, London. I was interviewing with their top team, the one in which one of the founder works, and, said by them, the most successful one. I didn't apply directly, I was invited by them. The all interview process was a lot of fun, even though quite tough, especially the last round. The first round of interviews was all about brainteasers, probability and general maths. Questions were not impossible but you have to be prepared and well trained to perform well. The second round focussed more on statistics and again probability, with a few questions on calculus. The last round was done via remote with the Chicago people (the team is splitted between London and Chicago). We started with some algebra problem to solve, not difficult per se but quite long (to be done on a whiteboard), then I got a quite difficult second interview, again on probability and correlation. The third interview was on game theory, whilst the very last one was on algos and C++. Overall I appreciated the all process, one of the most amusing I have ever participated in, even if they didn't make the offer. The London people were great, I would have liked to work with them. I think the main reason I didn't receive the offer was the C++ part of the interview, that's the only point at which I felt that I wasn't doing as I wanted to (and the guy's face was also confirming that). I know C++ and I have used it, but to solve quickly the problems I was faced with I needed a better fluency. The only thing that I have not liked at the end of the day was the fact that they (the HR of JT, those who wrote me at the end of the all interview process) didn't specify what was wrong with my interview, that's why I have to guess it. Anyways to connect myself to the latest posts I have to confirm that the people I talked to came from: Ecole Polytechnique (Paris), MIT, Stanford, Harvard and Cern. Hope this is helpful to someone. My advice to get a job in an HF: be super smart at maths, know a lot of stats and probability, and master C++ at a very deep level.
 
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farmer
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April 27th, 2014, 12:01 am

QuoteOriginally posted by: BudFox2013My advice to get a job in an HF: be super smart at maths, know a lot of stats and probability, and master C++ at a very deep level.I don't mean to dismiss what this guy is saying. He is probably smarter than me, makes more money than me, and gets to do some things I would have liked to do at some point in my life.But the reason this stuff is so hard, is it is very competitive. The analogy I would use, is if you wanted to get into the clamshell or candy bar cellphone business, in like 2007. You would have needed the cheapest manufacturers, the slickest marketers, the best everything, to try to go head-to-head with Motorola and Nokia and Blackberry and compete in what was a fairly mature if still fast-paced industry.Then Apple came along with the iphone. Just a thought, maybe you don't have to run a million miles an hour, if you can find an open lane, a new field, something far to the left of what everyone is doing the same. There may be bigger money, for people who are not as smart, in a less mature industry or niche of a mature industry.If the highway is jammed up, look for a side road. Somebody is making easy money somewhere with bad programmers. I am not saying it is easy to find such an opportunity. I am just thinking maybe you would see such an opportunity, and think this is not the mainstream, this is not what I heard about, this is not what my friends are doing, this is suspect to me because it is not what everyone else is doing.
 
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roundandround
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April 27th, 2014, 7:32 am

Thanks for your thoughts, everyone. There are at least one thing I am concerned about though. I come from a Scientific background, and as time goes on I am increasingly convinced that I am not at all prepared for the HF interviews, even though I may have the skills required for the job. I have spent ~6yrs in the PhD program (still not submitted by thesis, but am close to), and all these years, I have only trained and prepared for a career as a Scientist (or a Research Engineer). Interviews for scientific jobs are *very* different from HF interviews obviously. Questions are mostly focused on individuals' prior research work/publications, solving scientific problems (often in a collaborative way), discussion of specific (well known) algorithms, state of the art/field, etc. A lot of the emphasis is on what the individual can come up with on his/her own, rather than testing them on textbook concepts. Also over time, one loses touch with textbook minutiae, since one is focused on coming up with newer things rather than assimilating what has been done already. For ex., I don't remember the last time I touched a Linear Algebra or Calculus book, although I do know the (big/important) concepts well... All this is going to make it very difficult for me to do well in HF interviews as things stand now. Also, I have an offer from a startup to work for them. Since I don't have much work to finish in my PhD, I might just go for it, and finish my PhD work, and paper writing on the side. I may not have much time to prepare for HF interviews at least for the next 4-5 months unless I am done with all this. All this said and done, I do definitely want to give Quant job a serious try. I don't want to live with regret of not having done it for the rest of my life. Thoughts?
 
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neuroguy
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April 27th, 2014, 8:07 pm

QuoteOriginally posted by: roundandroundThanks for your thoughts, everyone. There are at least one thing I am concerned about though. I come from a Scientific background, and as time goes on I am increasingly convinced that I am not at all prepared for the HF interviews, even though I may have the skills required for the job. I have spent ~6yrs in the PhD program (still not submitted by thesis, but am close to), and all these years, I have only trained and prepared for a career as a Scientist (or a Research Engineer). Interviews for scientific jobs are *very* different from HF interviews obviously. Questions are mostly focused on individuals' prior research work/publications, solving scientific problems (often in a collaborative way), discussion of specific (well known) algorithms, state of the art/field, etc. A lot of the emphasis is on what the individual can come up with on his/her own, rather than testing them on textbook concepts. Also over time, one loses touch with textbook minutiae, since one is focused on coming up with newer things rather than assimilating what has been done already. For ex., I don't remember the last time I touched a Linear Algebra or Calculus book, although I do know the (big/important) concepts well... All this is going to make it very difficult for me to do well in HF interviews as things stand now. Also, I have an offer from a startup to work for them. Since I don't have much work to finish in my PhD, I might just go for it, and finish my PhD work, and paper writing on the side. I may not have much time to prepare for HF interviews at least for the next 4-5 months unless I am done with all this. All this said and done, I do definitely want to give Quant job a serious try. I don't want to live with regret of not having done it for the rest of my life. Thoughts?That all sounds very familiar to me. However while challenging, the interviews were not quite as impossible as they seemed before I started interviewing. The questions were mostly quite simple... far simpler than things I was working with day to day as a research scientist. The knack is to be able to work methodically and swiftly on such problems while in the mild discomfort of an interview. I have screwed up in interviews before on questions that I absolutely could always do given time and breathing space... but such questions get much harder when you are in a face to face or telephone situation where the pressure is on to get to the answer in 'real time'. So don't be intimidated by it, you will screw up, but you will learn. Even when I have screwed I have looked back and appreciated the idea of the question and grew to understand the strengths and weaknesses of my mind. This is a really good thing because in research your mind tends to follow the easiest channels and you go very deep into areas that you have a certain aptitude for. However quant finance (although this is a broad term) tends to require a large degree of mental flexibility rather than 'depth' (where by depth I mean spending large amounts of time thinking about a problem where there is no known solution and perhaps no known possibility of a solution).The key is simply training. Get the books, get textbooks, go on youtube and solve problems. Watch others solving problems. Get a friend to talk through brainteasers with you (my girlfriend helped me with this... she is really good at the brainteasers, so it was great because that gave me some mild pressure of not wanting to look dumb in front of her... so I had to do the questions with that going on). Treat it like training in sport rather than academic work. Throw yourself into the interviews. That will give you confidence when you do answer things well and teaching when you don't. After the interview make a note of every problem they gave you and then look at them again later on to understand where you could have done better. However... Farmer eloquently makes a truly excellent point. Don't equate hard questions necessarily with a great job. Often, the level of mental prowess required in interviews is a bit beyond what is needed day to day... (its a business and inevitably someone also has to change the oil and check the tyre pressures). Also a very narrow, hard and repetitious interview process can be a sign that whatever they are hiring for is quite specific and commoditised: i.e. they are looking for hamsters in wheels. And as we all know hamsters are fungible (at least if you are not a 5 year old girl) and wheels are infinite but bounded. So I think absolutely keep an eye open for the left field stuff. Warren buffet didn't get rich by drilling statistics problems on his saturday nights. Don't underestimate the importance of audacity, guile and grit. And be aware that the quasi-academic quantification of finance can be alluring but illusory. If you want to do it, do it. I refer you to the late steve jobs:QuoteRemembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart.
 
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DevonFangs
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April 28th, 2014, 4:27 pm

QuoteOriginally posted by: neuroguyQuoteOriginally posted by: roundandroundIf you want to do it, do it. I refer you to the late steve jobs:QuoteRemembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart.so find a loophole and buy a pancreas before someone poorer does?
Last edited by DevonFangs on April 27th, 2014, 10:00 pm, edited 1 time in total.
 
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farmer
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April 28th, 2014, 5:18 pm

These big trend traders don't have a correlation problem, or a math problem. They have a low-frequency kind of problem. There may be a lot of competition, and they may have taken on a lot of capital, but their main problem is that prices haven't made big moves lately.So they are pushing into higher-frequency, order management, better chop filtering to find the smaller trends sooner. What would really help, is some way to say something like Europe is not moving into a new regime, China is. Therefore, moves in the yuan are likely to travel, moves in the euro are not.So how do you quantitatively decide, in an automated way, which trends to overweight like this? One idea, is to go beyond time-series data, into text data. Machine orders go up, machine orders go down. But suddenly there is a new word appearing twice as often as "machine orders."
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