[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?

[dashboardfront]

> 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?

If this is a strong concern of yours you should probably go with one of the other two options. There's opportunities to make money in many industries. You might not end up with as high as a net worth as you would've if you went into quantitative finance, but that's not an absolute rule and you'll more likely burn out if you don't find satisfaction in your job. This isn't a dig towards anyone that is in the finance industry and enjoys their job, but it's common for people to end up quitting from dissatisfaction/work conditions.

Besides that, a robotics startup sounds pretty exciting.

[chollida1]

To be clear, I'm an engineering whose moved into the quantitative side, but am really poor at stochastic calculus, I haven't used it in 5 years. I wouldn't get a pricing job at a top their Investment house:) So I can't speak for the kind of quant job that most people consider to be the typical quant job.

Off the top of my head...

> 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?

First off your education counts for nothing when negotiating salary. If you can do the job, you get the salary for the job. Some people really have a tough time of letting go of this. I don't care if you have Phd or are a high school drop out, you get paid based on performance and role.

In Toronto, starting $100,000 with raise to 200,000 at the high end in 10 years.

Bonus is 0 - 2x that, expect about 0.75 . Alot of that would be based on the firms record and not your own. It doesn't sound like you'd be actually making money so you have a chance to be higher if you develop trading strategies.

That's great money but not the kind of money some people think. You don't get huge bonuses until you, yourself, produce even larger profits.

> 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

Connections really help. A Phd really helps. Writing a piece of open source software that a firm uses really helps, Writing a paper that the firm uses really helps. Jobs can be hard to come by as the industry is pretty incestuous. People move around alot and that means someone trying to break in has to answer the question of why hire you instead of the guy whose done this for 10 years and I know everyone he's worked for.

> 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?

If its for pricing, it probably won't help at all. If its a HFT then it helps.

Machine learning is used alot less than people think at most funds. Alot of people are under the assumption that you can just apply some machine learning to the market and make money, it just isn't possible for most people. There are just too many factors that can affect the price of a stock.

how do you model panic? Consumer confidence? Russia invading the Ukraine? OPEC selling oil below $80 a barrel? The US invading Iraq?

All of these affect the price of stocks, and its often modeled as an additional fudge parameter, that is positive or negative according to the whims of the modeler on that particular day, in other words, its a hack in the greatest sense:)

4) Just how much of the job is reading and implementing machine learning papers, and how much of it is general software engineering?

A lot less than you'd like. a ratio of 10:1 plumbing vs paper implementation is about what I do and I get to chose what i do:( If you think about it, the algorithm is small compared to the surrounding code you need just to get an order out the door.

Back testing can be 3 weeks out of 4 sometimes because for every idea you have that succeeds, you'll have 10 that fail at some level.

> 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?

Not touching this question with a 10 foot pole. I know I'm really excited for Monday mornings, others might not be.