As an undergraduate student, I studied finance and actuarial science. Finance is, of course, a practical and well-known degree, while actuarial science was a really nice course because it drew on the fields of mathematics, statistics, and economics; all areas that are very interesting to me. I finished my bachelors in 2016 and then I completed a Chartered accountancy course in 2018 (this is comparable to the CPA in the US or the RA in Europe). Once I was done with my professional qualifications, I took a job at Infosys Ltd. in their business finance division. The primary responsibilities were centered on deals advisory, financial planning and analysis, contract negotiations, and ad-hoc initiatives like tech automations and margin improvement plans. At that time, I moved from Calcutta to Bangalore and I worked there for about two and a half years.
Then I decided to move to a field where I could use my knowledge of actuarial science and become more specialized in the banking, financial services, or insurance sector. In early 2021, I joined Swiss Re in their finance reinsurance division, and it was a good fit. My primary responsibility was along the lines of a legal entity controller, but since I had skills in technology, I was involved in tech automation and reporting transformation initiatives. I worked with a lot of different tech tools and also mentored a cohort of colleagues to get up to speed with technology. I was with Swiss Re for about a year and a half during which I discovered the Certificate in Quantitative Finance (CQF) as a way to go further into the quant domain.
To summarize, the course takes applied mathematics, financial markets, and data science all together, which helps create an end-to-end skillset that can be leveraged to create real impact in any domain.
Basically, the CQF trains quants to develop a sophisticated set of tools and critical judgement with regard to mathematical and statistical models. In addition, we got immersed in data science; one might think it is a loop, but it is a spiral that does not end. Those modules covered a wide array of topics included supervised, unsupervised, reinforced learning, neural networks, and other techniques. I had some sense of these subjects, but I gained such good insights on the inner workings of the model. To summarize, the course takes applied mathematics, financial markets, and data science all together, which helps create an end-to-end skillset that can be leveraged to create real impact in any domain.
When I look back on the CQF, I think about how, throughout the course and in the final project, we were able to do things that we could ultimately use in a real-world scenario.
After finishing the course in March this year, I began to look for opportunities in Europe. I decided to join Deloitte in the Netherlands as a Senior Consultant in the financial advisory valuations and modelling (M&A) team, where we are currently working on sustainability and green energy projects. When I look back on the CQF, I think about how, throughout the course and in the final project, we were able to do things that we could ultimately use in a real-world scenario. In the first module, you will learn the fundamentals of quantitative finance, the second module introduced us to the Black-Scholes Model and showed us how linear programming helps in optimizing problems. In the trading strategy project, you will create and run your model, compare results with a benchmark for a given asset class, see how you are performing, and find out if it's really adding to the PnL or not. So, it is an iterative exercise and an enlightening experience. I look back on those classes as a transformative time for me and I look forward to continuing my work in quant finance in the years to come.