Before coming to Manchester, I thought I already knew why I wanted a career in finance. What I did not know was which part of finance would genuinely keep me curious. That became much clearer during my MSc Quantitative Finance at the University of Manchester. I chose the programme because I wanted more than a broad understanding of markets; I wanted to understand the models, data and reasoning behind financial decisions. Looking back, the course has given me stronger technical skills, but just as importantly, a clearer sense of where I want my career to go.

The course challenged me quickly. Quantitative Finance brings together mathematics, statistics, programming and financial theory, and these areas constantly overlap. A concept introduced in one lecture can become important later in another module or project. If I could give my pre-MSc self one piece of advice, it would be to stay consistent from the beginning and focus on understanding the intuition behind the formulas. I found that memorising a method was never enough. The real learning happened when I could explain why a model worked, what assumptions it relied on and when those assumptions might fail.

One experience that made this especially clear was building a DCF valuation of Sage Group. At first, the process looked straightforward: forecast cash flows, estimate a discount rate, calculate terminal value and arrive at a valuation. Once I started working with real company and market data, it became much more interesting. Every assumption changed the result, and every result needed a rationale. Using Bloomberg to collect and analyse data made the project feel much closer to the type of work carried out in industry. It also changed how I approached later work in portfolio analysis, credit risk and financial modelling. I became less interested in simply finding an answer and more interested in understanding what that answer was telling me.

My dissertation has been an even stronger example of that. I am researching the term structure of bond returns using government and corporate bond data. I expected the most difficult part to be the theory and econometrics, but working with real financial data taught me something different. Data is not always available in the exact form you need, an initial approach does not always work, and sometimes you must rethink your methodology. That can be frustrating, but it has also made me more independent and more confident in tackling problems with no textbook solution.

My Manchester experience has also gone beyond coursework. Studying alongside people from different countries and academic backgrounds has exposed me to different ways of thinking about the same problem. Group discussions, university resources and access to Bloomberg have all added to my experience. Being selected as a Global Futures Scholar also made studying at Manchester particularly meaningful to me and encouraged me to make the most of the opportunities available.

Most importantly, the MSc has helped me move from simply saying, “I want to work in finance,” to understanding what that actually means for me. I now know that I am most interested in investment research, valuation, portfolio analysis and financial decision-making. My advice to future students would be to treat the programme as more than a qualification. Use the resources, ask questions, explore careers early and use your projects to discover what kind of problems you genuinely enjoy solving. For me, that has been the most valuable part of the journey.

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