With a number of the leading banks re-architecting their platforms and systems, we have a variety of new and exciting roles for candidates from junior to director level. We have seen an increase in business facing roles and roles in the HFT space and further we are seeing that remuneration potential in the technology space is really starting to pick up. Lastly we have noted that many of the top firms are recognizing that they need to make the interview process quick and attractive to candidates. For this reason many clients are now turning around first interview to offer within one or two weeks and the interviews are a lot more hospitable and relaxed in nature than in previous years.

Emanuel Derman says it is essential for the aspiring super-quant to overlay theoretical knowledge with pragmatic common sense.
For the past few months, I’ve been teaching financial engineering at Columbia University, where I’ve been struck again by the difference between what can be taught in school and what can be learned on the job. Most of my quant generation arrived on Wall Street ignorant of financial theory; we began to learn its principles under the duress of having to quickly do something practical for someone on a desk. Nowadays, there’s an entire industry devoted to quant training. But in many ways, quantisation still requires apprenticeship, and so, for a recent conference talk, I tried to think about some of the things you discover when you finally put your training into practice.

There are very few laws
Unhappily, there are few unalterable laws of quantitative finance. No Maxwell’s equations, no Navier-Stokes. The only universally applicable law is that of approximate similarity, which states that the best estimate of the unknown market value of a security is the price of another security that’s closely similar to it. You need to find (or invent) a model to establish the similarity between two securities by demonstrating the equivalence of their future payouts under a wide range of circumstances. Most of the mathematical complexity in finance involves finding a decent description of the range of future scenarios.

Suspend disbelief
Although all you have is the limited power of this simple law, you must take your model of similarity seriously. Temporarily, like a fiction reader, you must suspend disbelief in your model. Then, when it’s complete, remind yourself that economics and valuation involve the behaviour of people, and think hard about what could go wrong.

Don’t get too carried away by mathematics
Mathematics is crucial to financial modelling, but it’s not central. It shouldn’t obscure the fact that finance and economics aim to be practical. If economics is about anything, it’s about the real world. For this reason, I often find myself questioning whether undergraduates should learn quantitative finance. Sensible modelling requires so much experience, taste and compromise that perhaps it should be postponed until students are more mature. Better as an undergraduate to learn solid concrete skills that are of unquestionable value rather a host of financial models that may be transient and incorrect.

The models you learned are the beginning, not the end
When I hired people to help value securities, I used to receive resumes from a headhunter who would market her candidates by rattling off mantras such as ?Knows HJM, knows BK, knows VAR, knows extreme value theory. What more could you want?’ What I wanted was not someone with an encyclopaedic knowledge, nor even the capacity to think fast on their feet, though both are good qualities. I wanted someone who understood that the famous models they had learned are not sacrosanct, that models were not the end point but the starting point. I wanted people who weren’t afraid of tinkering with the models they inherited, who were willing to invent their own. In most cases, despite the vast sophistication of published models, people on trading desks use simpler home-grown versions that make approximations to run rapidly and are modified to take account of the real world idiosyncrasies that weren’t part of the standard models’ assumptions. Models aren’t holy. You have to overlay known models with heuristics. You have to mess with them every day.

Data has no voice
Data alone doesn’t tell you anything; you need to think and theorise. Fischer Black once wrote: ?I find theory to be far more powerful than data when we’re trying to estimate expected return… When I read an empirical paper I usually seek out the theory section and ignore the tables.? Almost 150 years earlier, according to the physics Nobel prize-winner Steven Weinberg, Charles Darwin described a similar viewpoint in a letter to a friend: ?About 30 years ago there was much talk that geologists ought only to observe and not theorise, and I well remember someone saying that at this rate a man might as well go into a gravel pit and count the pebbles and describe all the colours. How odd it is that anyone should not see that all observation must be for or against some view if it is to be of any service!? So, be prepared to have a view, to make a theory. Data comes from the external world and must confirm or repudiate theories; theories come from you.

Abandon all hope…
Genuinely enthusiastic students sometimes ask what will happen when you find the ultimate model. On Wall Street, no-one knows what the correct model is, but they go ahead and price and trade anyhow. It’s a bit like the trial in Alice in Wonderland ? ?Sentence first ? verdict afterwards’. Black-Scholes is 30 years old and people are still debating its exactitude. Steve Figlewski recently wrote a somewhat tongue-in-cheek paper on whether a model with no principles at all was any worse than Black-Scholes. But practitioners don’t use Black-Scholes merely for its presumed exactitude; they use it because it provides a rational framework for thinking perturbatively. In a real job, you won’t have a 20-year time series to back-test your model. And even if you did, 20 years ago there was no volatility smile in equities, five years ago there was no smile in gold. So the model of 20 years ago and the model of today cannot be the same. A year ago I chaired a round-table session on smile models. In the past, I had often polled practitioners on which model they thought was the right one for the equity smile, but I could never get a consensus. So, at that round table, I simply asked both traders and quants their opinion of the best hedge ratio ? greater than, equal to or less than the Black-Scholes value. There was still no agreement. Fifteen years after the appearance of the volatility smile, it’s humbling to remember that we still don’t have a canonical model. As a result, we all have to be existentialists in matters of financial valuation, making our own decisions about what’s meaningful. There is no model-God, and he won’t give you the data to calibrate his ultimate model.

Models are powerful sales tools
One imagines that models are all about arbitrage, and that the right one can find you bargains and make you money. Sometimes that’s true, but models are equally valuable in dealing with clients and customers. Models are a helpful way of looking at the world. If you can get everyone to look at the world your way, then you can sell them things based on your views. This isn’t dishonest. It’s a reflection of the fact that the locus of financial value is vague and confusing, and any order you can plausibly impose on prices is immensely helpful to investors. Unless you can replicate perfectly and hold to expiry, a large part of value is in the mind.

Software is honourable
Academics often overemphasise models, but much of the success of a model depends on software engineering. It’s not hard to create a conceptually more advanced model. But how do you use it? You need live market data, historical time series, databases, input screens and calibration.As a result, for every financial engineer who works on a model you may need three or four more software engineers to make it usable. In modern markets, there is a very fuzzy line between model and software.

Form matters
In academic life, one likes to believe that content is all that counts. But even in universities, though truly stunning truths may become known no matter how uneloquently they are stated, form matters. Many finance professors like to recount with a strange mix of regret and pride how they need to publish in approved journals and in an approved style to get tenure. When models are used to establish the similarity of securities, to compare rather than predict, then persuasiveness is important. ?In the end,? according to Fischer Black, ?a theory is accepted not because it is confirmed by conventional empirical tests, but because researchers persuade one another that the theory is correct and relevant.? So, when you build a model, you have to explain it, in words. And until you can, you won’t completely understand it.

Between Feynman and Freud
What people most need to learn when they come to the practice of quantitative finance is how to overlay their theoretical knowledge with pragmatic common sense. Aristotle, in his Nichomachean Ethics, wrote that one should adopt a degree of precision appropriate to the subject. Though he was thinking of ethics, the same is true of quantitative finance. Until some unlikely future revolution, finding this middle ground is a practitioner’s major challenge. One must learn how to be neither too concrete nor too abstract, to choose some part of the spectrum between behavioural and quantitative, between science and psychology, between Feynman and Freud.

a) no clue about his technical/operational subject. If an interviewer is not able to judge whether a person is qualified for the job by having a good meeting, listening to his/her previous experience and the things he/she has done already in real live, it is already a sign that something went wrong in bringing this person on board. I know that “brain teasers” are an increasingly found bad habit and I presume that this is coming from this idiotic business consultant type of persons (a la assessment centers). Which again would support the thesis that they do not have real experience in their jobs and thus need such strange methods like brain teasers and puzzles – if the interviewer lacks the technical/operational qualification there the only method of judgment obviously is a “brain teaser”.

b) significant deficits in management skills: I want people who are honest, well trained and diligent … and the last method to find that out is a “brain teaser”. It is even questionable to find out whether a person is “intelligent” (what is the definition for intelligent ?) by such things. I don’t pay people solving puzzles and playing game (my little son is doing that, and he is in the right age for it)

But even more important in my opinion – it shows a considerable amount of disrespect concerning the interviewee. Im my opinion it is extremely unprofessional to play the “who has the longest …” game at work.

Fortunately I do not have to go these types of interviews (and hopefully I will not have to) … but if so, before working for somebody who runs around throwing “brain teaser” questions at people (not for fun but with the intent of making “judgment”) I would rather change my career path.

Do you ask a Fund Manager if he is good at “brain teasers” ? Probably not – you look whether the guy is successful in his track (which also involves alot of social intelligence to convince people) and whether his concepts are sound.

So when would there be the need to ask him a brain teaser question : only if you did not understand his concept and you were not able to make judgment from his track record, right?

Maybe … the questions which I should ask myself as a manager is, whether I want people who “accommodate” to every silly idea I have or whether I want to have a team of experts whom I can rely upon also when I am not in the office (which implies that the manager is capable leading a highly qualified expert group) … it is a question of leadership.

Clearly, a team needs a leader and the leader has the final decision … however it should be considered as a qualification of the leader to differentiate between things that are important (and thus his decisions are mandatory to all team members) and things that are just “baloney” (like “brain teasers”) …

I think, that if you are a quant expert you will always get a job … and those companies which do not consider you (despite your expertise, work experience and innovation capacity) just because you restrict your own thinking to that which is appreciated by your boss, you probably do not want to work for …

The assumption that the interviewer IS good is an idealized assumption (a bit similar to a model optimized on past data . If you look at overall fund tracks (cleansed of survivorship bias) the assumption that the interviewers ARE has statistically not too much evidence, does it !? (you could say the sales guys ARE per definition good …). That brain teasers are common maybe, which does not mean that they are bringing wrong results. The thing I m afraid of is, that the risk of a false negative, i.e. sending a real crack with a huge amount of knowledge away, just because he had a bad day or another one has heard the question before already is too high. Nobody knows what the real ratios in terms of false positive or false negatives with these types questions are … however they are used without actual figures at hand – and I suspect that many “interviewers” work like that – using techniques with no sufficient evidence of success, hit rates, and potential blow-up cases – also in their financial software. Indeed thats the way of creating a couple of mil loss easily …

When it comes to hiring, some employers act like they hold all the cards–and they can treat job seekers as poorly as they want, without consequence. They’re wrong: Smart employers know that good candidates have options (to say nothing of the ethical implications of being rude just because you think you can). Here are five common ways employers behave badly when hiring:

Having no regard for the candidate’s time. From last-minute cancellations, without apology or acknowledgement of the inconvenience, to not paying attention in the interview, some employers act like their time is the only time that matters. Most candidates go to a lot of trouble to prepare for an interview — reading up on the company, taking time off work, and often traveling–and their time should be respected too.

Not sharing their timeline. Employers have some idea of whether they’ll be getting back to candidates in a week or a month. There’s no reason not to share that information, and it can be agonizing on the job seeker’s side to have no sense of the timeline the employer will be moving on — and yet many employers keep job seekers uninformed.

Refusing to share their salary range, but asking you for yours. Employers know roughly how much they’re willing to pay; there’s no reason not to share that info, other than that they’re hoping to get you for a lower price. But that’s lame: If they lowball you now and you figure out later that you’re underpriced for the market, they risk losing you over it. They should tell you the range they expect to pay and put an end to all the drama and coyness.

Misrepresenting the work. Interviewers who make the job sound more glamorous or downplay less attractive aspects of the job–such as long hours–are guaranteeing they’ll end up with a bitter employee. Truth in advertising works to everyone’s advantage, because candidates who won’t thrive in the job, or the culture, can self-select out before they become your disgruntled employees.

Not notifying candidates that they’re no longer under consideration. This is both common and inexcusably rude. Candidate are often anxiously waiting to hear an answer–any answer–and end up waiting and waiting, long after a decision has been made. It’s about simple respect and courtesy (and it just doesn’t take that long to email a form letter).

Wish you great interview experiances and good luck with a job search!

If you get most of the skills above – your employment as quant is assured

Overall, quants are required to have multiple set of skills and technologies. The main function of a quant in the business is to develop and maintain financial models, do quantitative analysis. If you are working in a large organization in the quantitative position – quantitative skills is all you need, all the rest will provided by the IT departments. The situation is totally different if you are working in smaller offices. You would need a much more diverse set of skills and VBA probably would be one of them.

Though I do believe that – yes, Ivy League grad quants get the best and highest paid jobs.

Why?

The reasoning behind is simple:

Factor 1: Proven record of excellent (top in the world) performance. If you were good enough to get in and, more importantly, do well in the Ivy League school means that you have a set of qualities  that allowed you to outperform your competitors in the past (everyone wants to get into the Ivy Leagues – only actually able t0).

Factor 2: Better technical, technological, theoretical, educational background. You can argue all day long that one gets as much out of school as one desires (or how much is determined). The truth is that an average Ivy League grad is much more determined, much more disciplined, much better prepared educationally than students from other schools. Why? They study in the much more competitive environment, with much stronger professors. They use the latest and greatest technological advances since their schools can afford to implement them in the programs faster then other schools.

Factor 3: Its hard to determine skills based on the resume and interview. Ivy League diploma is basically a certification of a good quality. Four point zero GPA from an Ivy Leauge schools is in fact a fair proof that you do know what you are expected to know after years of school. It does guarantee that you are a highly motivated individual and you can work really hard to excel.

Factor 4: People tend to hire grads from the schools they went to. Wall Street is full of Ivy League grads, especially top firms. Loyalty to the own school is very common.

After all said and done – not getting into the Ivy League should not discourage you from studying. Ivy League does neither guarantee a job, nor protects you from a lay off. It just opens a door for more opportunities.