Before starting my career, I completed my Bachelors of Engineering in Computer Science from RGM College of Engineering and Technology and then began working for TATA Consultancy Services as a software engineer. I served on the Center of Excellence team, where we worked on various problems and developed solutions for our clients. I spent three years there and gained a lot of insights and coding skills, but I was looking for further challenges and decided to find something that would be a combination of finance and programming. I did a lot of research and finished an MBA in financial engineering from IFMR Graduate School of Business, KREA University. Then I did an internship at Credit Suisse India, where I worked with the valuations team and from there, I went into model validation at Deutsche Bank. At this point, I had known about the Certificate in Quantitative Finance (CQF) since my MBA, and decided it was finally the right time to join the program.
I knew there would be quite a bit of math to learn and I really liked the Maths primer prior to starting the course. It was so thorough and well taught that I believe anyone, even from a non-mathematical background, could learn the basics and jump right in to the CQF.
Naturally, I knew there would be quite a bit of math to learn and I really liked the maths primer prior to starting the course. It was so thorough and well taught that I believe anyone, even from a non-mathematical background, could learn the basics and jump right in to the CQF. In the course itself, Module One gives a wonderful foundation on quantitative finance and I still go back to those resources to review certain ideas and check to see if I'm on the right path. I’m very interested in pricing, so I spent a lot of time on Module Three and Module Six, which covers many aspects of models and their implementation. I found that Module Three develops the concepts from the mathematics perspective and served as a foundation for Module Six, where different models were taught.
Through the CQF and intensive study, I developed a very good understanding of pricing and, I have since taken a job in market risk model validation with Morgan Stanley.
Then in Module 6, I wound up using many of the techniques we had learned and implemented my final project Basket CDS pricing in Python from scratch. I felt very good about how much we learned on the course. Through the CQF and intensive study, I developed a very good understanding of pricing and, I have since taken a job in market risk model validation with Morgan Stanley. I am an Associate and I work on different Value-at-Risk models. Some of them are related to the pricing world and this is where I connect a lot of the things I learned on the CQF.
These days, I look back on how much fun it was to meet so many people from around the world. During the course, we had a few WhatsApp groups and it was useful to discuss the lectures and explain the ideas and techniques to each other to reinforce our knowledge as we went along. We often did this on the weekends, (when we were still working!), and it was a great way to let the concepts settle in. The important thing is that the CQF will provide you with a lot of dense knowledge and high-quality resources, but you have to utilize them regularly and keep your skills fresh. That's where the Lifelong Learning comes in. When I visit the CQF website or the CQF Institute now, I always find that there are new things to see and attend. So, I keep on reading and watching the videos as they come along. I have spoken to many people during and since finishing the course – including people in New York and London, and we have a great community. Your colleagues can open up a whole new world for you because everyone has different skills and areas of expertise to share – some from math, some from finance, some from computer science, and even some from physics, and with these kinds of conversations, you can find ways to connect it all together in interesting and meaningful ways.