
How to Get a Quant Job in Finance - saraha
https://www.financejobs.co/development/how-to-get-a-quant-job-in-finance.html
======
Rainymood
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 1994 to 1999. Following which he worked for the Royal Bank of
Scotland from 1999 to 2005 as a quantitative analyst at a variety of levels,
finishing as the Head of Quantitative Research for Group Risk Management. He
joined the Centre for Actuarial Studies at the University of Melbourne in
November 2005 as an associate professor and is now a full professor.

Pure math to quant to actuary. It's kind of funny how I see/have met so many
people that have gone this route. It's like they got sucked into pure
mathematics, then thought: shoot I really need to get some practical work done
with this math! And afterwards, at a later stage in life they wanted to back
out and wanted a stable, high paying, but still math heavy job (actuary!).

Does anybody here have any experiences with being on the 'front office'? I'd
love to hear them.

~~~
branchless
Pure math to head of QR risk management over the period when RBS took on
insane leverage and relied on wholesale lending markets staying at low rates
forever. An assumption that was incorrect and led to RBS being bailed out by
UK tax-payers.

Good work fella.

~~~
shamney
head of quantitative research = spending all your time on pricing models for
complex derivatives. it's unlikely he had anything to do with RBS's overall
financial strategy

~~~
branchless
Head of modelling risk for RBS. Maybe some of it was derivatives. Some of it
should have been modelling the cost of borrowing money on wholesale markets.
The RBS strategy was leverage to the hilt, borrow short, lend long. Lunacy.

~~~
cma
Maybe he thought it was a bad srategy until he calculated in the likelihood of
tax-payer bailout if things went wrong.

------
chollida1
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...](http://www.amazon.com/Heard-Street-Quantitative-Questions-
Interviews/dp/0970055277)

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 your hand. You'll know every way to look at a derivative product. You can
program in matlab. You create the next big thing like CDO's or CDS's

If you love math, this is where you want to be.

2) Quants who trade You work at a hedge fund. You can program in python with
scikit or use R. You don't know calculus maybe as well as a pricing quant but
you know some area of the market much much better. You know stats like its
your mother tongue.

3) Risk quant/programmer. You do modelling all day for a trader or risk
manager. You can take a portfolio and model any feature that someone ask for.
Var, beta, greeks, you can spit them out quickly. You know C++, excel and R.
You might have been an engineer in a former life.

This is often considered the low position in the quant hierarchy. This is how
some programmers break into the industry.

4) I lied there is actually special category 4). This is the professors of
quants. You sit around all day and think about the next big equation, or how
to model derivatives better than black scholes. If you are one of these people
chances are your name is known in the industry:)

 __EDIT __someone asked if you can do these without a math degree. The short
answer is probably not. YOu 'll need a math, engineering, or physics degree,
unless you really are driven to learn hte math yourself.

A comp sci degree might work but you would be the exception and you'd be
fighting up hill. Ask your self honestly, how many branches of mathematics
have you self taught yourself and you'll get your answer. For most the answer
is none, for a select few the answer is yes.

Someone asked about brain teasers. They do get asked, you have to deal with
it, whining in an interview that this type of question gives no useful hiring
indicator won't get you hired. I don't ask them but they are common:(

We throw around brain teasers at work during the day trying to stump each
other. I guess some of it is trying to look smart. Some of it is that we just
really like to dissect any problem and figure it out.

I think the biggest reason for asking brain teasers is that at a hedge fund
there are no rules for how to make money, excluding legal. They want people
who can think outside the box. it turns out that its really tough to test for
"can the candidate think outside the box"?

Trading is a higher stress job than most other math or programming jobs, what
we are trying to see is not only are you smart, but can you think on your feet
and not get stressed out, because if an interview stresses you out, whats
going to happens when a $50,000,000 position that should be going up starts to
go down.

Are you going to complain that the model says it should go up and the market
isn't being fair, or are you going to accept what's happening and get back to
work? You'd be surprised at the number of people who chose option 1.

I ask a bunch of questions that some people feel are brain teasers. In between
questions I'l throw out what's the square root of 225 to see how the candidate
reacts. Good traders seem to be exceptional at mental math, with very, very
few exceptions.

I'll also ask alot of probability questions. Get ready to know what your
expected payoff is if you gamble on dice games, its basically what you do
every minute of the day when trading:)

Now if you are a programmer, how do you get into the industry? You need to
know stats, machine learning, and programming, really well.

And I don't mean know machine learning, like "I tool an nlp library and
stiched it together to do sentiment analysis on a corpus of text". I will ask
what algorithms the underlying library used. You used SVM, great talk to me
about your kernel selection methods. I want to know that you understand the
math, and more importantly the assumptions and limitations of the library you
are using.

The reason is that when you trade on a model that is based on your machine
learning, I want to know that you know when it breaks. Finance is an industry
that loves to model but has crashes that are predicted to happen 1 in 1000
year events happen every 10 years.

I love helping programmers who want to become quants get into the industry.
Please feel free to ask if you have questions!

~~~
abrichr
This article and discussion are quite timely for me, as I was recently offered
a position as a "quant analyst" at a derivatives arbitrage firm -- from what I
understand, similar to 3).

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?

~~~
gjm11
> How can you reconcile the opportunity cost to society [...]

There are two fine institutions that may help with this: taxation and charity.
If you earn a lot of money, then you pay a lot of taxes and can afford to give
a lot away.

Everyone likes to complain about taxation, but it's what turns a free market
full of individuals and corporations all (to a good first approximation)
trying to maximize their own wealth into something that benefits everyone.

And the most effective charities seem to be able to save a life (or provide a
kinda-equivalent amount of other benefits) for something in the vicinity of
$2000. (Important cautionary note: all such figures are very rough and you
shouldn't trust them too much.)

So, suppose you have a choice between a quant-finance job of, let's suppose,
exactly zero social value, and a job in medicine that pays $50k/year less. And
suppose you'd be equally happy in either aside from ethical concerns. Then by
taking the quant-finance job and giving away all your extra earnings, you give
your government (let's say) an extra $20k/year to spend on schools and
hospitals and police and roads -- and, unfortunately, various other things you
might approve of less, so let's say it's the equivalent of $10k/year going to
something obviously valuable like teaching, so you're paying for about 20% of
an elementary-school teacher. And you give an extra $30k to (I hope) very
effective charities, so maybe you are saving 10 lives a year.

So it comes down to the question: is the medical job more beneficial to the
world than 20% of an elementary-school teacher and 10 poor Africans' lives per
year?

You might answer that either way, but at any rate I don't think it's obvious
that the answer is that the medical job is better.

(Some notes: I am not claiming that anyone who goes into finance in preference
to another worse-paid job is _obliged_ to give away all the extra money they
earn. Only that doing so is _one option_ , and that it might work out pretty
well ethically speaking. It might be psychologically difficult to give away so
much of one's earnings. It might be harder to "derive meaning and
satisfaction" from things as indirect as tax and charity, compared with
deriving them from one's actual work. It can be argued that quant-finance has
positive social value, but I'd be skeptical of claims that it has _much_. I do
not work in finance and never have, though being a mathematician it's always
possible that one day I might.)

~~~
NhanH
If the financial job in question is an exact zero sum job (with 0 social
value), then neither taxation nor charity would help at all, because
essentially while doing your job you're already causing a negative value to
someone else. And even if you give always ALL your wealth, you'd just return
those wealth back to society. In essence, after a lot of shuffling of wealth
around, you still didn't contribute anything. (And again, that's an "IF". I'm
not stating whether the premise - financial job is a zero sum game - is
correct or not.)

~~~
makeset
Doubtless there are roles within the financial industry deserving of the
"vampire squid" label in social impact, but I'd expect them to be fairly
obviously unethical if not outright illegal on inspection (deceptive sales of
risky exotic derivatives comes to mind). Quantitative pricing of exchange-
traded products (e.g. HFT) is much harder to argue against, IMO. To me it
seems like a clear net positive to process information better than others to
offer more competitive prices to anonymous buyers and sellers, because
tightening spreads means more efficient markets through better price
discovery. It doesn't sound very different than a retailer undercutting prices
by running a leaner business than the competitors and afford lower profit
margins. Of course the competitors are screaming bloody murder, but how is
this not good for society?

------
rhodri
I'd like to see an article titled "Why Get a Quant Job in Finance?"

~~~
kasey_junk
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?

~~~
abrichr
I think the question that rhodri poses is an important one. For me,
satisfaction comes from more than just the solution to the immediate problem,
or the amount of money I'm being paid to solve it. Satisfaction comes from
knowing that what I'm spending my life on is meaningful in some greater sense.

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.

~~~
lucozade
> I think the question that rhodri poses is an important one.

And I think kasey_junk answered it pretty succinctly. That's not to say that
it's the only valid reason to want to do anything. It just happens to be quite
a common reason for people to become quants.

If what you want to do is answer the great questions in life then I'm not sure
that being a quant is necessarily going to help with that.

------
seongboii
There's another way that hasn't really been mentioned yet, and that's to work
for yourself via your own quantitative trading system or through Quantopian's
crowdsourced hedge fund
([https://www.quantopian.com/managers](https://www.quantopian.com/managers)).
Disclaimer: I'm an employee there

------
graycat
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 opportunities for mathematics in
finance.

Jerked the chains of lots of headhunters and got nowhere.

I concluded f'get about it.

Maybe since then it's changed.

~~~
deathflute
Hey, I wanted to send you a message but your profile has no email. Can you
send me a message or put an email in your profile? Thanks.

~~~
graycat
How do I send you a message?

~~~
deathflute
You can find my email in my profile by clicking on my username.

~~~
graycat
I do see URL

    
    
         	www.localmeze.com
    

but I see no e-mail. When I do the same click on my Hacker News user name,
then, yes, I see a line for my e-mail but the line (a _text box_ ) is empty
because I haven't given my e-mail to HN.

What's going on I don't know.

~~~
deathflute
I did not realize that the email is not publicly displayed. I have put in the
about section now.

hn At machine.imap.cc

~~~
graycat
Just sent to

hn At machine.imap.cc

------
mlrtime
"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.

~~~
branchless
95% of quant code is not only fails to achieve low-latency, it's utterly
incorrect, unmaintainable and frankly deranged.

~~~
ratsbane
That might also be true for 95% of _all_ code.

------
javaistheworst
Not mentioned but important is coding - if you are a quant in an IB these
days, you will be expected to do some level of coding - often C++, Python, and
maybe some Excel VBA to plug things in for the desk to play with, while IT
production-ise the rest intop the RMS and trading apps.

~~~
gadders
> while IT production-ise the rest intop the RMS and trading apps

Generally to software engineer it so it performs properly, can be put on a
grid, etc etc.

------
ivanche
" Brainteasers are popular." Can somebody explain why? I'm genuinely
interested, since in software dev interviews we see less and less
brainteasers...

~~~
murbard2
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 defined problem, you're going to struggle
with the more complex issues we deal with.

~~~
waterlesscloud
Can you give an example or two of the more complex issues you deal with?

I honestly have no idea.

~~~
murbard2
Can't be too specific, but I often need to solve differential equations, find
closed form solutions to some integrals (mathematica helps but is often not
sufficient), build models which strike a good balance between realism and
tractability. Find which properties are important to preserve in models and
which aren't, etc.

chollida1's comment is spot on and I oscillate between 1 and 2.

~~~
FD3SA
None of these have any similarity to brainteasers. In fact, these are all
excellent subjects on their own that could be tested directly. For example:

"Give me an overview of a numerical method used to solve differential
equations."

The more time you spend asking brainteasers, the less time you have to devote
to your actual skills of interest. You may even find that quizzing someone on
mathematics may help refresh and solidify your own understanding.

~~~
murbard2
Do you have experience interviewing candidates?

Many candidates will answer this question correctly and yet be totally unable
to do anything when they're confronted with a non textbook case. To be clear
the brain teasers I ask are mathematical problems, not the type of brain
teasers used in consulting interviews. For instance:

We play a game where we each draw a secret random number uniformly between 0
and 1. We each may re-throw if dissatisfied with our first throw, or me may
keep it. We do not know whether or not the other has chosen to re-throw. We
then compare our results and he who holds largest number wins $1. What is the
best strategy to follow?

That's the type of brainteaser I'd ask. It's accessible to a good high school
student. I interviewed a PhD candidate in applied mathematics from a top Ivy
league university who:

\- wouldn't believe that maximizing the expected value of the number obtained
wasn't optimal until shown an explicit counterexample

\- was unable to write the equations properly or model the problem

\- was unable to solve the equations after I handed them out to them

He _was_ however able to talk about his thesis work. Your questions wouldn't
have caught that at all. His thesis work was in game theory.

~~~
megrimlock
Ok, I'm stumped. If I've parsed your description correctly, we get no info
about our opponent's actions or the results until the end. Absent any ability
to observe their strategy, it seems like you do want to maximize for expected
value of your own actions, and I'm curious about the counterexample.

How do we maximize EV? A single throw's pdf is 1, for x in [0,1], so its EV is
0.5. The question is how to improve on a single throw by deciding to re-throw.
A re-throw is independent and gives the same EV. We want a strategy that gives
us higher cumulative EV. Say our strategy is that we have a threshold A, where
we re-throw any result below A. Because x is uniform, the probability that we
re-throw is also A. The cumulative EV of the strategy is A * EV(second_throw)
+ (1-A) * EV(keep_first_throw). Since we only keep the first throw for results
in [A,1], the EV for that event (integrating x * pdf from A to 1) is (1+A)/2\.
So EV of the whole strategy is A/2 + (1-A) * (1+A)/2\. It has max EV when A is
0.5, giving EV of 5/8.

So how do you do better?

~~~
foldr
I don't really get it either, but I think the idea is that you're supposed to
somehow optimize on the assumption that your opponent is equally likely to be
using any strategy. (To my mind this makes the problem very confusing because
it's such an unrealistic assumption.) If you assume that the only possible
strategies are "rethrow if my first throw is <= x", then you can define a
function f(x,y) giving the EV where x is the threshold for your rethrow and y
is the threshold for your opponent's. I guess you then integrate with respect
to y (to get the EV for any given x assuming a uniform distribution for the
opponent's choice of y) and then differentiate the result with respect to x to
find the maximum? If the solution is along those lines that would explain the
problem's supposed accessibility to good high school students.

~~~
murbard2
And no, you don't assume that every strategy is equally likely. You assume
that the opponent plays optimally with full knowledge of your own strategy,
which means you're looking for a Nash equilibrium
[http://en.wikipedia.org/wiki/Nash_equilibrium](http://en.wikipedia.org/wiki/Nash_equilibrium)

~~~
foldr
That's fine if you already know game theory, but then the problem is not quite
as accessible as you made out. You can't expect someone to just invent the
concept of a Nash Equilibrium on the spot from the words "best strategy".
(Although I understand that you may expect your interviewees to already be
familiar with game theory.)

~~~
murbard2
In this case, the candidate's thesis work explicitly dealt with game theory.
In addition, this doesn't require much knowledge of game theory... if you
think about the problem for a bit, you can re-derive the concept fairly
easily.

Nash's brilliance was in proving that under reasonable conditions, a mixed
equilibrium always exists, which is far less obvious.

~~~
foldr
The issue isn't the difficulty of the concept but the ambiguity of "best
strategy".

~~~
murbard2
Fine, best strategy given a perfectly rational opponent. There, ambiguity
removed.

~~~
foldr
Well not quite, you also need to specify that the opponent assumes that you
are perfectly rational, and that he assumes that you assume that he is, and
that he assumes that you assume that he assumes that you are, and so on. A
perfectly rational opponent would not assume that you are perfectly rational
in the absence of any relevant information. Rather, he would assume that his
opponent was equally likely to be using any strategy.

------
tacoman
Darn. I thought this said "a Quaint Job in France" and I clicked on it. I was
disappointed.

~~~
codeordie
Ha - same thing happened to me!

------
kofejnik
commenting to bookmark (hope this is permissible here)

~~~
grayclhn
FYI: if you upvote the article, it will be listed under the "saved stories"
link on your profile:

[https://news.ycombinator.com/saved?id=kofejnik](https://news.ycombinator.com/saved?id=kofejnik)

(which I can't see, by the way)

------
0800899g
great quant info in here

------
nether
I'm filled with envy and rage over the fact I'm not good enough at math for
this. I barely did above average in my grad level math courses in college
(studied aerospace engineering). I worked my ass off; I should have breezed
through it if I had the ability to be a quant.

~~~
mahyarm
Math classes I've noticed just take more time because there is more detail to
them, but if you keep up with the workload and go ask TAs questions at labs,
it can be done. Out of all of my university courses, the math classes were the
most workload heavy. They were 2x the workload of almost any CS class I took.

------
poppow
The whole industry is in recession. Those stuck in it just mental masterbate
about crap, knowing that automation/ai will destroy their paradigm. In reality
it's boring and nothing like the energy and passion in start ups/creative
tech.

