My background was in engineering. Numbers just resonated with me - right from my early days. When I started my career in banking, I worked in market and liquidity risk, mostly traded market risk. It entailed a lot of numbers, and I wanted to really understand the market. I always wanted to know what we were doing, why we were doing that, and what the best practices were. So, I always had that natural curiosity. That's what has pushed me to deepen my quantitative skills, especially in statistics and machine learning. That's what pushed me, or challenged me, to become a quant.
When I found the CQF and looked through the program outline, every single thing was in there - I loved everything about it. It gave me a great technical foundation. It improved my confidence in engaging with more complex models, derivative pricing, and machine learning applications in finance and risk management. It changed how I look at things. I like to break things down into smaller problems to figure out, but the CQF really trained me and helped me understand a lot of assumptions. It helped me know when to challenge those assumptions. It has been critical in my current role because I do a bit of model risk management and it's helped me to challenge models. It made me realize that it's not enough to just be able to build a model, you have to be able to challenge it appropriately and adequately too. So, the CQF has been very critical to my career. It's helped me transition into model risk management, which is something I really enjoy doing right now.
I acquired many skills from the CQF. One thing I enjoyed very much was the stochastic modeling. I really loved time series analysis. I enjoyed learning how to write a lot of programs using different machine learning techniques. I learned a new skill, which was how to use Python. A lot of tools that the CQF has made available to me have helped strengthen my ability to do my work critically and properly.
