I did my bachelor’s degree in economics and finance as a double major at the University of Melbourne in Australia. After graduation, I went into the banking industry, where I was a consultant for a few years. I enjoyed the work but realized that I was more interested in the investment side, especially given how the finance industry has evolved in recent years. So, I decided to pursue a master's degree in data science at Monash University, which is also in Melbourne. Coincidently, when I was on a trip in China, I met someone who was enrolled in the CQF program, and it seemed like a very good match for my interests and academic pursuits.  My bachelor’s degree and master’s degree coursework were providing a great foundation, but I wanted to obtain more specific knowledge on the industry. I also was working to bridge the gap between the fundamental part of finance, with the more quantitative and data driven areas.  

Once I began the CQF program, I found that some areas were fairly familiar to me; nothing was easy, but it aligned very well with the rest of my studies. For example, prior to beginning my graduate degree, I had done an introduction to Python online, but I had not used it at work. However, the CQF projects used a lot of Python, and this reinforced my programming skills very effectively. There can be a lot of critical adjustments to make, especially if you are running a complicated program; it could take 3 or 4 hours to run the entire thing, so good coding practices and careful time management are critical.

After completing the CQF, I wanted to find a role that would be somewhere between a full-time quant position and doing fundamental research. This is how I landed in my current role. I am a portfolio analyst working for an asset management firm, where we do a fair amount of modeling, as well looking into the credit quality of corporate bonds in the global market. It is very helpful to be able to browse through the entire credit universe and use both perspectives in the analysis. The CQF definitely really helped me in the transition, not to becoming a quant per se, but in bringing elements of data science and financial analysis together. Before doing the CQF, I didn't know exactly how these skills could be utilized, but that became clear over time and has led me to a very interesting career pathway.  
 

Looking back, the CQF had two key selling points for me. The first was the ability to see the videos on demand. Living in Australia, it would have been very difficult to see the live sessions, but it was easy to fit the videos into my schedule and also to go back to them periodically when I wanted to study certain aspects in greater detail.  The other key point was the Lifelong Learning library. Now, I can always go back to the library and pick up some new knowledge when something interests me. In my current role, I am working with a model that we built for corporate bonds, looking into pricing, valuation, and relative value. This takes quantitative factors into account, but it also includes an understanding of the current economic conditions and the specific situation of the company itself. When a model is telling you that something is relatively cheap, or relatively expensive, you should not simply rely on the model; you need to think about the qualitative aspects of the results as well. I remember very vividly that there was one component of the lifelong learning library that was talking about behavioral finance and how, when the news comes out, different investors behave differently and that can contribute to how the markets will move. I find that component very interesting because you can incorporate behavioral finance into your modeling too. 
 

In the future, I want to continue to do what I'm doing now, being able to blend the quantitative and the qualitative modes of analysis and improving on my quant skills at the same time. The CQF has certainly shaped my goals in these areas. I recommend it highly and what I would say to prospective candidates is to plan ahead and have a good understanding of how the program is structured. I felt that the first half of the program was harder, but then I got used to the routine and was able to take advantage of both the course videos and the libraries; good time management will always pay off!