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.