What’s your background? What did you study?
I took courses in Economics and Mechatronics Engineering at The University of Melbourne. I then studied a joint PhD program between The University of Melbourne and University of Birmingham, where I was affiliated with the Electrical Engineering and Computer Science departments. My research topic specialised in the realm of control engineering and mathematical optimisation.
What sparked your interest in working for a trading firm over Academia?
Reflecting on my academic background, a common theme would be that of systematic decision making – whether it be human or automated. There seemed like no place better than a trading firm where that happens a lot in actual day-to-day practice. Doing research at a trading firm, the feedback cycles also tend to be faster than in academia, so there’s never a dull moment and you can switch projects with a quick turnaround. I also regarded there to be better prospects for early-career progression and stability, working in industry generally.
Tell us about your experience in the Graduate Quant Research Program.
I joined SIG during COVID lockdowns, so my onboarding consisted of 3 weeks of Zoom classes on options trading basics. After that, I went straight into doing research and learning on-the-job. When borders opened, I was able to travel to SIG’s US headquarters in Philadelphia. There, I spent roughly 2 months going through a dedicated Quant Researcher training program, where we were taught different datasets and tools used in research at SIG, as well as additional machine learning and options pricing theory.
Tell us about a typical day for you.
I can normally be found working on my current research projects. Projects typically involve putting data through back testing or analysis pipelines, then scrutinising the outputs for the purpose of evaluating a strategy or validating a hypothesis. In the early stages of a project, I may be thinking more about the project’s scope, methodology, and ways to innovate on our trading strategies. Throughout the project, I could also be documenting the results and sharing them with the team or chatting with developers and traders about strategy implementation and deployment. Occasionally, an ad-hoc idea or question might arise, which allows me to run a quick study on that.
Are there any specific skills or qualities that are essential to succeed in Research at SIG?
We work with data on a daily basis, so overall savviness and intuition when reasoning about data is naturally needed. Projects may also require their own specific domain knowledge, from how particular financial instruments work, to how exchanges disseminate their high-frequency market data. It helps to have the resourcefulness to pick that up and apply any new assumptions to the analysis. The teams of trading, technology, research are all stakeholders of the trading system, yet each group differs in expertise. Collaborating effectively across these areas is essential, so it’s important to be able to communicate well between them.
How would you describe the growth you’ve seen in your time at SIG?
There’s certainly been growth in the impact of my work. The first few projects I worked on didn’t have much consequence to SIG’s trading, although I still learned a lot from doing them. My more recent projects, however, have gone on to affect SIG’s trading strategies in a very core way via a component of our option pricing model. The research I do now leverages earlier work, whether it be re-using some older code or an analysis technique. Over time, I’ve also gained familiarity with our quant tools and datasets. As a result, my efficiency in doing research has also grown.
What do you like best about working at SIG?
I’m enjoying the opportunity to tackle complex quantitative problems that aren’t always well-defined to begin with. For instance, we may be trying to determine whether some perceived effect in the markets is real. Given the noise inherent to financial markets, the approach is not always clear-cut, often requiring creative and critical thinking. It’s a blast working with terabyte-scale datasets and having the computing resources to process them effectively. The culture at SIG is also very collaborative. It feels like every team wants every other team to succeed, across different business units and even across offices.
What projects and experiences have been most rewarding so far?
A few projects have involved collaboration with the US team, where we’ve adapted some of their findings over to APAC markets. It’s then always an exciting yet nervous time watching a strategy that you’ve worked on enter production trading. The most rewarding feeling is monitoring those strategies and seeing them perform as expected from back tests and pilot tests.
What three pieces of advice would you give to a graduate interviewing at SIG?