Why I chose mathematical finance after computer science

After I joined the University of Toronto's Master of Mathematical Finance program, I kept hearing the same question: why math finance when I came from computer science at Waterloo?

It is a fair question. I spent years learning how to build software, and I still love building things. But the ground beneath software engineering is moving. My feeling is that AI tools are taking over more of the routine work that used to define a traditional software engineering job. That does not make engineering unimportant. It makes me ask where I can use those skills on problems with a deeper connection to a real domain.

Why finance?

I wanted to move toward a field where technical work meets an applied world: where a model, a system, or a decision has to survive contact with data, uncertainty, and real consequences. Finance is that world for me. I love the pace of it, the questions it asks, and, honestly, I love the money too. I am curious about how markets work and how better tools can help people make better decisions.

Where I hope to contribute

I am exploring opportunities as a quantitative researcher, quantitative developer, or quantitative trader. Each role connects mathematics, code, and markets in a different way. I have written a companion article comparing the three as I work out where I fit best.

What draws me to all of them is the chance to apply my skills to improve finance: to make research more rigorous, trading systems more reliable, and decisions more informed. The MMF program is a way for me to build the mathematical and financial foundation to do that well.

I have made one concrete choice: I accepted a quantitative developer internship at BMO. I wrote about the two offers and the interview process in a separate post. I am still learning where I want to grow from there.