My background is in market risk. I worked in banks in Paris before focusing on derivatives pricing and risk models. I had done a math foundation, which is a two-year program, in Vietnam. Then at university, I did a bachelor’s in risk management and followed this up with a master’s in financial markets, both in Lyon, France. I became interested in the CQF because all the knowledge I had acquired on risk models had been learned on the job, on the fly. So, although I enjoyed the work very much, I wanted to do an official program to get a deeper understanding on a few areas of quantitative finance that really captured the scope of quant work. I also did not want a program that just focused 100% on math or a general finance program either. I had a good background but knew I could take my career to a more advanced level. 

What I liked the most about the CQF was the focus on the practical projects, which brought all our learning into practice. I liked the machine learning module the best because it was a new area for me, and it has opened the door to so many interesting and exciting opportunities since then. Whilst I was completing the CQF program, I was working in London at Open Gamma, which is a solution provider to buy-side firms. We helped companies to optimize their capital and collateral requirements on their derivative portfolios. This job had many relevant ties to the CQF module on derivative pricing and taking time to study this topic more deeply helped me to understand the models and what was going on behind them much more clearly.

After I finished the CQF, my job changed at Open Gamma, and I moved into a new position as a Quantitative Product Manager. In this role, I oversaw building out quantitative solutions on our platform. I managed the product road map and worked with the quant engineering team as well as our sales and client services team to deliver solutions to our clients. The CQF was directly connected to what I was doing at that time, and it was also very helpful when I later changed jobs again. Most recently, I have moved to Bloomberg to work on a Python-based platform for quantitative analytics and data science in finance, called BQuant. I'm working now as Head of BQuant Product Specialists in EMEA. We are a team of passionate quant developers, helping our clients to get the most value out of the BQuant platform.  Now, I am working on a Python-based platform for quantitative analytics and data science. I learned Python during the CQF, especially as I worked on the projects we completed. That kind of immersion helped me to become comfortable with Python and stay up to date with the latest trends in quantitative finance. It really fits right in with my day-to-day work now. 

The CQF certainly opened the door for me to different areas, from derivative pricing to quantamental trading. For me, one of the best features of the program was the flexibility, since we could watch the lectures on the weekend at a convenient time because it's all recorded. The second aspect was the emphasis on the practical side of finance. The projects were designed in a way that you could easily find a use for them in real life. Also, being able to discuss this work was useful in job interviews. I was able to apply the knowledge quickly in my last job and now I am doing so again at Bloomberg. Finally, I would like to mention the networking – we have a great community from our student cohort, and we have a WhatsApp group that we are still using to keep in touch. Anytime we have a question or want to discuss something, we can share it in the group and that’s always a nice way to connect with people we met during the CQF program. For incoming students, I would just say, be prepared to work hard, but make sure you take the time to get to know your colleagues and classmates as well. It is part of the full CQF experience.