I studied business management as an undergraduate at the University of Manchester and although I did not focus on financial engineering at the time, I slowly drifted into working with financial data in my first job at Nikkei in London. I liked working with financial data providers and after some time, I made a move to Bloomberg, where I started working on the help desk. There, I truly found that I had a passion for financial derivatives. I was taking customer questions on various aspects of Terminal functionality related to derivatives, with a particular focus on pricing from plain vanillas to interest rate swaps and anything with optionality. Since I did not have a background in financial engineering, some of this work was rather complicated and I wanted to gain a better understanding of the mathematics behind these functions and calculations. So, I enrolled in a part-time degree in finance at the London School of Economics. I made another job move as well and joined IHS Markit, shifting my career path towards the role of Pricing Analyst.

Around this time, I heard about the CQF and after pondering it for a while, I decided it would be a good move. The curriculum and pacing matched up with what I was doing at IHS Markit very well. I found that it’s one thing to understand different pricing formulas and apply them in a very systematic manner. It’s quite a different thing to have a greater appreciation of the derivation of the various models, to really grasp their strengths and weaknesses, and implement them with that deeper level of insight. When you first start out in the direction of quant finance, you may find the mathematics scary. You can pick up a book on financial engineering and try to teach yourself the basics, but the barriers to entry are high. I also tried several courses to learn more about calculus, especially PDEs and the like, but that was also not quite what I needed. Finally, I looked at some of the CQF lectures that are provided on the program portal, and I thought that the information was very accessible and could provide solid building blocks to the more advanced concepts. The program really delivers on its promise to help financial professionals like myself get up to speed quickly and be able to move forward with much better foundation across the math, finance, and programming aspects of quantitative finance. In addition to gaining knowledge on the math front, I was also learning Python, which has been very useful since then.  

The morale was good across our cohort, and being in touch with other students, I could see that I wasn't the only one who was working late on our assignments and coding projects. In fact, we still use our WhatsApp group, which is great for socializing and professional networking, even five years after we have finished our CQF program. These days, I access the lectures regularly just to keep up to date.  

I’m now working as an Investment Risk Analyst at Nikko Asset Management Group in Tokyo. The CQF made a difference in this move because they use Python for many things from automation and data cleaning to developing internal models. In terms of advice for incoming delegates, I’d say that you should pick a project that challenges you and will give you a set of relevant skills for the future. The CQF faculty have done a very good job in creating a range of projects that are tailored towards different needs and suited for diverse career paths. If you push hard on your final project, you will produce a very nice piece of work, something substantive, where you can clearly demonstrate your skills and understanding of a particular problem in quant finance.