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Find a job without internship experience?

Joined
3/29/08
Messages
77
Points
18
I'm a student from a top CS program, and will get a master degree in 2009 (but in fact this is a PhD program, the master is just a mid-way degree). My research is on machine learning and statistical data analysis.

I'm considering the possibility of finding a quantitative finance job in 2009 based on my statistics and computer science background. But my 2008 summer intern will go to Yahoo!. I mean ... I already accepted this internship several months ago, so I cannot change it (and apply other finance internship).

In other word, in 2009 I will try to find quant jobs without intern experience in finance. Is this possible?

What I can do to increase the chance? I plan to enhance my knowledge and skills on C++/econometrics/time series analysis/Matlab, using my spare time from now to next Spring. Is this the right direction?
 
An internship at Yahoo is as far from a trading desk as it can be. It's possible to get into finance but something has to give. At some point, you will need to make a turn from your path. You are not going to learn about the financial market while working on how to improve Yahoo email spam filter or Yahoo messenger interface unless you are working in Yahoo! Finance part this summer.
It can be argued that after the Yahoo internship you can apply for an IT job at an IB doing model development but is it the path you envision?

What I can do to increase the chance? I plan to enhance my knowledge and skills on C++/econometrics/time series analysis/Matlab. Is this the right direction?
How a Yahoo internship has anything to do with econometrics/time series/Matlab? You will learn plenty there, for sure, but how much of it will directly correlate to your finance job?

One of the things students like us seem to forget is that we focus too much on the technical side of it while not paying much attention on how the market works and how we fit in the big picture.

A good book I like to recommend is The Complete Guide to Capital markets for Quantitative Professionals from Master reading list for MFE - QuantNetwork - Financial Engineering Forum
 
An internship at Yahoo is as far from a trading desk as it can be. It's possible to get into finance but something has to give. At some point, you will need to make a turn from your path. You are not going to learn about the financial market while working on how to improve Yahoo email spam filter or Yahoo messenger interface unless you are working in Yahoo! Finance part this summer.
It can be argued that after the Yahoo internship you can apply for an IT job at an IB doing model development but is it the path you envision?

What I can do to increase the chance? I plan to enhance my knowledge and skills on C++/econometrics/time series analysis/Matlab. Is this the right direction?
How a Yahoo internship has anything to do with econometrics/time series/Matlab? You will learn plenty there, for sure, but how much of it will directly correlate to your finance job?

One of the things students like us seem to forget is that we focus too much on the technical side of it while not paying much attention on how the market works and how we fit in the big picture.

A good book I like to recommend is The Complete Guide to Capital markets for Quantitative Professionals from Master reading list for MFE - QuantNetwork - Financial Engineering Forum


Andy, thanks!


I mentioned Yahoo! just to indicate that I cannot go to any finance internship this summer. For example, I mentioned the internship position you post, which is soooooooooooooo good. But I even cannot try, because I already accept the summer intern at Yahoo! :(

So, what I mean is that, if no 2008 summer internship in finance --> no job possibility in 2009, then I will give up this idea.

If there is still a chance, I'd like to try. I will do everything possible in my spare time --- from now to next spring --- then I will try to get some job interview.

The Complete Guide to Capital markets for Quantitative Professionals --- Yeah! This is the first quant-related book I bought (3 weeks ago when I surf at Amazon.com). I will bring this book with me this summer !
 
A finance job without relevant internship is possible but extremely difficult to get this day and time. Your hope would be to rely on the firm's website. We all know that all the best jobs are not advertised online but through personal contacts.
That's why students in MFE programs have an upper hand when it comes to competing for these kinds of internships, jobs.
Another thing I notice is that relatively few MFE students go through a rotation training program that big IBs set up for their incoming analysts. Most of them seem to go right into jobs hitting the ground running.
In one way, it's good that you don't spend much time on chores like coffee ordering or shoe shinning. On the other hand, it means that firms like to have people who can come in and contribute right away.

If you have a prior finance internship, they don't have to teach you all the jargons and show you how to use Bloomberg. In all way, an internship with any Wall Street firm is so much more valuable than anything you do with Yahoo, Google for that matter.
 
At this point, it's very hard to get a finance internship anyway, so don't let that stop you making the most of the Yahoo experience. Certainly you should do the internship, as Andy says it has a lot less value, but it still is good.
First up, you need to hedge your chances of not making it into sort of finance job you want by building your options in the IT industry.
But Yahoo is a brand, and these have several layers of value.
As a headhunter, my preconception is that that you had to beat a lot of your peers to get this job. We like to see that sort of thing, it says that someone who knows your game thought you were much better than a large % of people who play it.
There are two main paths open to you in the medium term.
First is the classical quant route, where you will be competing with people who've done more of the 'right' sort of maths, and you are right to compensate partly for this with better programming. But you will still need that sort of maths.
This is the most liquid market, and where most people in this game end up.

But you are mildly well set up for a more specialist job at a hedge fund or proprietary trading operation, either standalone or in an IB.
There is good money to make from looking at vast piles of data trying to work out what is going on.
But it's more specialist, which means that there may be no job for you, or several good offers.

You appear to be thinking of quitting at MS level, and that's probably sub optimal, unless you really hate your PhD. A MS in CS makes you a quant developer, or rather someone who can learn to be a QD, which is an OK job, but possibly you can aim higher.

If you are at the stage of choosing your PhD topic, then I would give serious thought to using and building skills to identify signals in series of price data. To be done properly you'd understand 'market microstructure', not technical analysis.
 
At this point, it's very hard to get a finance internship anyway, so don't let that stop you making the most of the Yahoo experience. Certainly you should do the internship, as Andy says it has a lot less value, but it still is good.
First up, you need to hedge your chances of not making it into sort of finance job you want by building your options in the IT industry.
But Yahoo is a brand, and these have several layers of value.
As a headhunter, my preconception is that that you had to beat a lot of your peers to get this job. We like to see that sort of thing, it says that someone who knows your game thought you were much better than a large % of people who play it.
There are two main paths open to you in the medium term.
First is the classical quant route, where you will be competing with people who've done more of the 'right' sort of maths, and you are right to compensate partly for this with better programming. But you will still need that sort of maths.
This is the most liquid market, and where most people in this game end up.

But you are mildly well set up for a more specialist job at a hedge fund or proprietary trading operation, either standalone or in an IB.
There is good money to make from looking at vast piles of data trying to work out what is going on.
But it's more specialist, which means that there may be no job for you, or several good offers.

You appear to be thinking of quitting at MS level, and that's probably sub optimal, unless you really hate your PhD. A MS in CS makes you a quant developer, or rather someone who can learn to be a QD, which is an OK job, but possibly you can aim higher.

If you are at the stage of choosing your PhD topic, then I would give serious thought to using and building skills to identify signals in series of price data. To be done properly you'd understand 'market microstructure', not technical analysis.


I really appreciate your reply ... valuable information!
 
If it's at Yahoo! Finance it might look pretty good on the resume.

Unfortunately, this is a job about machine learning and data mining research, since I didn't think about hunting a finance-related internship at the time of applying summer intern.

Anyway, I'm still hoping that the project they will give to me is something related to finance, e.g., statistical learning on a time-series data set ...... but that's the best, and almost impossible, case.
 
At this point, it's very hard to get a finance internship anyway, so don't let that stop you making the most of the Yahoo experience. Certainly you should do the internship, as Andy says it has a lot less value, but it still is good.
First up, you need to hedge your chances of not making it into sort of finance job you want by building your options in the IT industry.
But Yahoo is a brand, and these have several layers of value.
As a headhunter, my preconception is that that you had to beat a lot of your peers to get this job. We like to see that sort of thing, it says that someone who knows your game thought you were much better than a large % of people who play it.
There are two main paths open to you in the medium term.
First is the classical quant route, where you will be competing with people who've done more of the 'right' sort of maths, and you are right to compensate partly for this with better programming. But you will still need that sort of maths.
This is the most liquid market, and where most people in this game end up.

But you are mildly well set up for a more specialist job at a hedge fund or proprietary trading operation, either standalone or in an IB.
There is good money to make from looking at vast piles of data trying to work out what is going on.
But it's more specialist, which means that there may be no job for you, or several good offers.

You appear to be thinking of quitting at MS level, and that's probably sub optimal, unless you really hate your PhD. A MS in CS makes you a quant developer, or rather someone who can learn to be a QD, which is an OK job, but possibly you can aim higher.

If you are at the stage of choosing your PhD topic, then I would give serious thought to using and building skills to identify signals in series of price data. To be done properly you'd understand 'market microstructure', not technical analysis.


Sadly, there is a real dilemma for people like me ...

1) people always say, if you quit your PhD at MS level, you will not get a decent quant job because a MS in CS can only get a quant developer position.

2) people also say, most of the knowledge you get from you CS PhD degree is not useful for a quant job ... i.e., it might be a waste of time (at least not a time-efficient way) of spending 5/6 years on your PhD.

Interestingly, most knowledge in CS PhD is judged useless, but we are still required to finish the PhD in order to be a good quant.

How about I quit with a Master degree, but I still show that I'm a good researcher like a PhD? e.g., I might published several research papers at the time of MS graduation. I know CS papers are useless for finance, but at least a MS in CS with several good papers should be treated the same (?) as a PhD in CS.
 
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