In Conversation with CQF Alumni: Thomas Kuehn

Tell us about the work you do in your current role?

It’s all sorts. I am coding data processing tools and building analytical models as well as writing credit rating criteria. We are now also dabbling in machine learning algorithms, which can be useful at the exploratory stage of criteria development, before methodologies are expressed in the usual, more tractable and easier-to-communicate generalized linear model framework.

Why did you decide to pursue your current career path? How did you get to the senior level that you’re at today?

I didn’t actively pursue this path, rather I ended up in the current role by accident. I was unemployed after resettling from Dublin, where I had managed the index-tracking cores of Pioneer Investments’ European fixed income funds on a maternity-cover contract basis. I was looking for a job in the treasury risk management and asset-liability management space – as documented in the Praise section of the recently published second edition of Moorad Choudhry’s banking bible, “Principles of Banking”. My current employer – one of the Big Three credit rating agencies – required a specific econometric skillset in combination with analytical subject-matter experience for a one-off project on a contract basis. The project took a couple or so years longer to wrap up than anticipated, as a result of which my contract morphed into fixed employment. I have been moving teams ever since, which has given me quite a rounded set of skills and experiences and is one of the benefits of the transferable skills I had previously acquired. 

What do you enjoy most about your current role?

It’s very varied - a wholesome mix of research, quantitative analysis, and coding.

What are some of the biggest achievements or highlights in your career to date?

In the run-up to the Global Financial Crisis, I successfully prevented a number of investments in mis-sold products. I had just joined my first employer, a universal bank, as risk manager and had to safeguard shareholder value and more broadly stakeholder interests. I consequently refrained from certain riskier investments which worked in my favor.

What have been some of the biggest challenges in your field of work over the past few years?

To make sense of the promises of artificial intelligence, or even just machine learning. This is a fast-moving topic which has invited a lot of outrageous promises. It reminds me a lot of the dot-com bubble, where the mere mention of AI in the company name pushes up pre-seed valuations of startups. The other challenge is ESG, which is currently all the rage, but which arguably ultimately carves out alpha for those investors who are able to invest in unregulated alternative assets. It may well amount to a wealth transfer from retail investors to the nimbler fast money and should prove a fertile future hunting ground for litigation lawyers. The challenge here is to concentrate on the service, which is to enhance transparency, whilst abstracting from the potential societal effects.

How has the quant finance / finance industry changed throughout your career to date?

Risk-taking has obviously been curtailed, and with the quantification of almost all risk components the universe of arbitrage strategies has shrunk considerably. With the advent of off-the-shelf analytical solutions risk managers and controllers have effectively been commoditized, leaving only the cutting-edge quant geniuses at the very top of the pyramid to reap substantial pecuniary rewards. 

You earned the Certificate in Quantitative Finance (CQF) in 2004. Where did the CQF add value to your career? 

The knowledge I acquired in the course of my CQF studies – and the alumni activities ever since – enabled me to speak quants’ language, which has given me the confidence and skills to successfully work alongside and with quant teams throughout my career. 

In your opinion, what are the skillsets that are required to be successful in quantitative finance / the financial industry today and do you think different skills will become more important over the coming years? 

I don’t think the required skillset has changed, it is still mathematical analysis, product and market knowledge, coding, and creativity. One positive development is greater inclusion, especially with respect to neuro-diverse people, which is absolutely fantastic and should be celebrated.

What would your advice be to someone starting a career in the industry?

Become an expert, i.e. complete qualifications in a niche of the field you are most excited about. Everything else will follow. One should obviously be able to code efficiently, so Python, SQL and database technology are essential core competencies.