Ce (Ryan) Wang


Graduated:  2017


What was your MCS program of study?

Specialist Program in Statistics


What did you learn while at UTM?

I feel fortunate that I became a person who loves mathematics and statistics during my time at MCS. This has been one of the most important motivations for me to pursue graduate studies and a career with passion. The curriculum in MCS also trained me to deal with subject matters with rigour, which has been continuously helping me to prepare for various challenges. Life in MCS was also a great training to undergo a large amount of pressure. 


Can you share with us your career story to date?

After graduating from UTM, I completed a Master in Engineering in 2020 in the Department of Mechanical and Industrial Engineering at U of T, with a focus on Operations Research. 

I have been working in several positions on quantitative risk modelling for financial institutions in Toronto. My role has been developing, validating, analyzing, and interpreting risk models, including both stochastic models and predictive models. Currently, I am a senior quantitative analyst in the treasury modelling team at TD.


Any advice on career planning and job search?

I'm going to narrow down to the field of quantitative finance so, as to not be too generic. Most of the jobs in this field in Toronto require a master's or PhD degree in quantitative subjects. Therefore, it's reasonable to make GPA maintenance a top priority during undergraduate studies. However, there are multiple things one can do to prepare for a career in quantitative finance during undergraduate, such as seeking internships and preparing for professional certificates. It's also helpful to start developing one's coding skills, as models and methodologies in quantitative finance are all implemented in computer programs. Programming languages such as Python, C++ (common for numerical methods such as Monte Carlo simulation, and numerical PDE, with needs in computational efficiency), SAS (common when a large amount of data is stored on a Unix server), are popular tools. Matlab and R are also being used in the industry. SQL is useful since pulling and manipulating data is necessary for nearly all kinds of jobs in the field.


Can you share any industry insights?

In my opinion, quantitative risk modelling is a good field to pursue a career. If you enjoy learning mathematics, statistics, and coding, there is a good chance that you will enjoy building or validating risk models.


How can MCS students connect with you? 

You are welcome to reach out to me with any questions.



ryan wang