For many professionals considering a transition into quantitative finance, one question often comes up: “Do I need a traditional finance background to succeed?”
For SMU Master of Science in Quantitative Finance (MQF) alumnus, Caden Lee, the answer was no.
Coming from an Aerospace Engineering background, his journey into quantitative finance was anything but conventional. Yet today, his career spans derivatives, quantitative strategies, front-office trading support, automation, and stakeholder engagement, a path shaped by curiosity, adaptability, and a deep appreciation for how mathematics can be applied to real-world financial markets.
Discovering Quantitative Finance Through the Global Financial Crisis
Ironically, his first exposure to derivatives came through studying one of the most turbulent moments in financial history — the Global Financial Crisis (GFC).
“The GFC served as a cautionary tale about the dangers of financial engineering and excessive confidence in mathematical models,” he shared. “But despite those negative lessons, I was fascinated by how mathematics and engineering principles were applied in finance.”
What began as intellectual curiosity soon evolved into a serious interest in derivatives and quantitative finance.
“To me, derivatives remain an important financial innovation that continues to play a critical role in supporting global markets today.”
Why He Transitioned Deeper Into Quantitative Roles
Before pursuing MQF, he worked in derivatives sales and business development at the exchange. But over time, he realised that global financial markets were becoming increasingly quantitative in nature.
“I wanted to strengthen my quantitative skills and gain a deeper understanding of how quantitative methods are actually applied in finance,” he explained.
That realisation ultimately motivated him to pursue the SMU MQF programme and transition into more technical front-office quantitative roles supporting trading desks.
His professional experience has included supporting LNG and crude oil trading desks on pricing, hedging, and quantitative strategies, in environments where technical depth and commercial understanding are equally critical.
Succeeding in MQF Without a Finance Background
One of the biggest misconceptions about quantitative finance is that candidates must come from finance or mathematics backgrounds to thrive.
His experience suggests otherwise.
“Quantitative finance is highly interdisciplinary,” he explained. “Financial knowledge is only one component among several important disciplines.”
Coming from engineering, he was already accustomed to navigating multiple technical domains, something that helped him adapt to the rigour of MQF. Still, he describes the programme as exceptionally demanding due to its breadth across mathematics, programming, statistics, and finance.
Rather than focusing on memorising everything at once, he believes success comes from mastering the foundations first.
“For me, probability theory, stochastic calculus, and programming were especially important. Financial concepts can be learned progressively over time.”
His advice to prospective students? Don’t be intimidated if you do not come from a traditional finance background.
“I do not believe a traditional finance background is strictly necessary to succeed in MQF.”
The MQF Modules That Made the Biggest Impact
When asked which modules proved most valuable in his professional work today, two stood out immediately: Credit Risk Models and Financial Data Science.
“Credit Risk Models was one of the few modules that provided meaningful insights into how models are applied in real-world settings, including lessons from the Global Financial Crisis,” he shared.
Meanwhile, Financial Data Science gave him practical exposure to working with real financial time-series data including the pitfalls and nuances practitioners often encounter in industry.
“These are things every practitioner needs to be mindful of.”
Beyond Technical Skills: Learning How to Learn
Interestingly, one of the biggest takeaways from MQF was not a specific formula or programming technique.
It was learning how to continuously adapt.
In his professional experience, many of the quantitative challenges he encountered involved unfamiliar asset classes and models that extended beyond what was formally taught in class.
“The programme taught me how to identify domain experts, ask the right questions, and learn effectively from practitioners around me,” he said.
He also credits MQF for helping him become more comfortable reading research papers independently and building frameworks to quickly understand new asset classes.
“In quantitative finance, professionals constantly encounter new products, models, and market dynamics. One of the most valuable lessons from MQF was learning how to learn continuously.”
Why Commercial Understanding Matters for Quants
While technical expertise remains essential, he believes successful quants today must go beyond building sophisticated models.
“No matter how sophisticated a model may be, it is ultimately meaningless if others do not understand or support it,” he reflected.
For him, the best quantitative professionals are those who can bridge technical depth with business understanding, collaborating effectively with stakeholders while ensuring models remain commercially relevant and practical.
That balance between quantitative rigor and real-world applicability continues to shape how he approaches his work today.
Leadership Beyond the Classroom
Beyond academics, he also served as the representative for the part-time student cohort on MQF’s Executive Leadership Committee while balancing a demanding career.
The experience taught him valuable lessons in stakeholder management, advocacy, and time management.
“Many students in the part-time programme had families and children, so I worked closely with the programme managers to suggest welfare initiatives that better suited the cohort.”
Simple gestures such as organising food and ice cream during class breaks helped foster stronger social connections among students navigating similarly demanding schedules.
The Future of Quantitative Finance: AI, Systems Thinking, and Adaptability
Looking ahead, he believes the future of quantitative finance will increasingly revolve around AI applications and systems thinking.
“Agentic AI is one of the most exciting trends because it is transforming how we work and shaping how quantitative finance may evolve,” he shared.
While traditional machine learning focused heavily on model building, he sees the next frontier as designing collaborative AI frameworks capable of solving complex problems more effectively.
“I believe the next generation of quants will need stronger systems-thinking capabilities alongside traditional quantitative skills.”
A Bridge Between Academia and Industry
Reflecting on his MQF journey, he believes the programme accelerated not just his technical development, but also his professional maturity and approach to problem-solving.
“The programme broadened my perspective on the industry through interactions with both faculty members and classmates from diverse backgrounds.”
Learning from professors who were also active industry practitioners helped him approach quantitative theories through a practical lens rather than a purely academic one.
Most importantly, MQF taught him to think more critically about assumptions, trade-offs, and limitations, lessons that continue to influence his work today.
And if he had to summarise the value of MQF in one sentence?
“MQF is a valuable bridge between academic quantitative training and Singapore’s real-world finance industry.”
