I found the transition fairly straightforward, although it did take me a few months to completely settle.  I am glad I made the move and so far I have really enjoyed my PhD. My workplace was extremely busy and organised and I found academic life to be a lot quieter and a little more….haphazard (in some respects). There are a whole range of ‘transferable skills’ that can be taken from the workplace straight into a PhD. Presenting & organisational techniques I learnt whilst working, alongside many other attributes from the world of work have supplemented and enhanced my PhD over the past two years. On a day to day basis, what about the similarities and differences between The Real World and PhD life?

Similarities

Mostly I find myself working 9-5 although sometimes I do work the odd evening and weekend (no different to when I was in The Real World). I have goals, targets and deadlines to meet. I am responsible for the work I am carrying out, I need to make sure everything is organised and conducted properly. I do get stressed about how much work I have to do. I worry about how well I have completed tasks and about what other people think about my work.

Differences

Being a PhD student gives you the freedom to explore, to make mistakes and learn freely, that time wasn’t available when I was working. In my PhD project, I am the boss, I own it and it is up to me what happens to it. I can plan my work and get on with it without being dragged into meetings and moved projects because the company plan has changed. One negative difference I find is the solitude of a PhD, although I work in a lab full of people I am working alone on my project and sometimes that can make me feel incredibly lonely. There are no rewards (not really, until you finish). No matter how much work you do, you will never get a pay rise, the best you can do is get the satisfaction of getting a paper published, getting your name on a patent and/or giving yourself a big pat on the back.

They both have problems and they both have good points, good days and bad days. If you want to leave work to escape pressure, I am not sure that starting a PhD is the answer. If you are incredibly passionate about a particular subject, can work without instant gratification, can work well on your own and are sure the move is right for you, then go for it!

Keeter makes many pertinent points which are applicable to any quasi-academic job. In order to make a better transition, Keeter mentions two aspects to keep in mind. Firstly, a graduate program may not give enough opportunities to work in teams and collaboration and team work has to be inculcated. Secondly, one may get used to speaking and writing to academic audiences. It is essential to get more training to write for nonacademic audiences. The interview also highlights the importance of being able to juggle many responsibilities at the same time. This is something which I feel academic do have good training in what with handling research, teaching, getting funding and administrative matters.

One aspect which is not really discussed is to have a better business sense of the research area even if one’s own research is more foundational in nature. This is because even if one is working in a research group within a company, business interests run supreme in many instances.

The interview suggests ways to get an entry into quasi academic jobs. It is helpful to intern in appropriate organizations during graduate study and also to keep in touch with one’s relevant professional association.

The application in recommendation systems is also outlined with the example of the recent Netflix contest where $1 million were offered to anyone who could significantly improve the company’s recommendation system. The article also highlights some well know companies where statisticians are well sought. Among them are Google:

“I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.”

Opportunities at IBM are also mentioned:

I.B.M., seeing an opportunity in data-hunting services, created a Business Analytics and Optimization Services group in April. The unit will tap the expertise of the more than 200 mathematicians, statisticians and other data analysts in its research labs — but that number is not enough. I.B.M. plans to retrain or hire 4,000 more analysts across the company.

In the article, related fields such as mathematics, economics and computer science are also highlighted. The main take home message is that numerical skills are not required in traditional fields such as the insurance industry but in other businesses too.

This is not particularly good news for final year PhD students who are starting to explore various job possibilities. Some friends who were doing quantitative jobs in the London financial sector have been laid off. I have not heard much on how research funding in the UK will be affected but some state universities in the US will not be planning to do any hiring for the next year. The following is an excerpt of an email from a director of a research centre in a US university:

There is, unfortunately, a reality constraint that reflects what has happened during the last couple of days on the financial market. To explain, even before the stock market began its downward plunge, the state was already caught in a serious budgetary crisis reflecting the earlier mortgage and housing situation. (As you may have heard from the news, some people on the state payroll have not been paid since the beginning of July and the state is seeking a huge loan to avoid bankruptcy.) In particular, this budgetary crisis has extended into all of the public sectors within the state…

Lance Fortnow analysed things similarly in a recent entry (Fiscal crisis):

Schools like Northwestern have structured their finances so they can weather short term shocks in the economy but a prolonged recession or depression will start to take its toll. New faculty hiring will likely be the first victim in universities. Tenured professors may delay their retirement after taking a hit in their CREF accounts holding up slots for new hires. Universities worried about their long-term finances may also slow down opening up new positions. On the other hand we should see increased enrollments on every level which may push the need to hire more CS faculty. Computer Science has lost its luster in recent years, but still reliably produces jobs and there is a flight to safe majors in times of economic turmoil. People who have trouble finding a good job often go back and get their Master’s while they wait out the recession. We should also get more people interested in Ph.D.s as alternatives dry up.

Meanwhile, Kenneth Arrow has written a comment piece on the recent credit crunch. He points out the “growing difficulty of understanding the underlying risks imposed by growing complexity“.

He felt that applicants for banking jobs need to demonstrate interest in finance either by personal reading or specialized workshops. Among quantitative tools he particularly emphasized partial differential equations, probability & statistics, stochastic and programming skills. He also emphasized basic knowledge about stocks, bonds, call/put options, interest rates and inflation. He commented that some knowledge of Capital Asset Pricing Model, Black-Scholes model and Gaussian Mean-Reverting Short-Rate model is helpful.

Compared to other speakers, Dr. Karasinski gave tips (here, here, here, and here) about certain books to read. Among magazines, he recommended Risk, Financial Analyst Journal and Wilmott. He commented that additional dimensions of knowledge of psychology, economic history and business are crucial for any mathematical PhDs applying to be a quantitative analyst.

There were a couple of more focussed presentations on algorithmic trading which is of particular interest to me. There is great development in this area and it is something that has also attracted considerable interest in the computer science academic community. On the banking side, algorithmic trading involves complex modeling of historic movement to predict the future, and analysis for hedge strategy. There is also a scope of exotics which includes pricing of complex financial instruments and risk management techniques. One speaker emphasized proprietary trading as one of the key areas of focus. Overall, the presentations were insightful enough to provide a flavour of what life in an investment bank is like.

The whole event was well managed and it was surprising to find over two hundred students there who were doing postgraduate or doctoral studies. I was sitting next to a German man doing a PhD in economics and a Spanish man doing a PhD in Biology. Most of the presenters were from the top investment banks (Deutsche Bank, Morgan Stanley, Barclays Capital, Goldman Sachs, HSBC, JP Morgan etc.). They had done PhDs in scientific subjects and then switched to banking. A chemistry researcher raised concerns that it seemed that investment banks seemed inclined towards mathematics and physics PhDs. Although the speaker gave examples of how varied backgrounds were welcome, it did seem evident that mathematical PhDs were desirable because of the complex modelling and quantitative analysis involved in the job.

Although most talks were about the London financial sector, there was one presentation about the numerous opportunities in Qatar and other centres that are part of Gulf Cooperation Council. There was an interesting comment by Dr. Jessica James from Citi was that in the banking sector, mathematical PhDs are almost like the classics degrees of the pervious era, a sign of an ‘intelligent’ and ‘useful’ person. She gave a relatively technical presentation on how to make better predictions.

One common piece of advice was for PhDs to take part in an internship program so that they can see if the financial sector is the career for them before taking a plunge. I was particularly impressed by the presentation of a Polish physicist who switched to Wall Street and has a major role in HSBC. I will try to convey some useful advice he gave in my next entry.