Career Case Study: Jason Chong — Actuary turned AI Engineer

Jason Chong's career is proof that an actuarial qualification can take you further than you might expect. 

The Quantium Senior Data Scientist and newly credentialled Fellow shares how the FIAA shaped his path into AI engineering — and why actuarial thinking remains his sharpest professional tool.

Why did you become an actuary?

It started with mathematics. I had always loved how numbers could tell a story, not just describe what happened, but help you anticipate what comes next. Actuarial science felt like the most rigorous way to apply that instinct in the real world. Risk, uncertainty and probability are things that shape every major decision a business or government makes and actuaries are the people trained to make sense of them.

There was also a practical element. FIAA is globally recognised and I liked the idea of a credential that would hold its value across industries and borders. What I didn’t anticipate was how much the core skills would translate directly into what I do now in data science and AI.

I studied actuarial science at the University of Melbourne and for the most part, kept my head down on clearing exams during and after university. The early part of my career was fairly traditional: general insurance pricing as well as designing and building dashboards. I enjoyed it, but I kept gravitating towards the data and engineering problems, the ones that involved scale, complexity and a bit of ambiguity.

The first real turn came when I moved into product analytics and started building ML models properly, things like data pipelining, feature engineering and model diagnostics. That opened a door.

Once I understood how to build and deploy ML systems, the leap to AI felt natural rather than sudden. Working on one of the company’s first AI applications, with no prior experience in the space, was the moment everything shifted. Suddenly the questions I was asking were different: not just what does the model predict, but how do we know when to trust it, how do we monitor it in production, how do we make it robust enough for thousands of users across multiple regions. Those are the questions I’m still working on today and I don’t see that changing anytime soon.

My first actuarial job was an internship at Deloitte during the summer break at the end of my second year at university. I applied through the standard recruitment process, prepared by polishing my resume and reading up on what actuarial consulting involved day-to-day which, as a student, you don’t really have a clear picture of until you’re actually in it.

The internship came and went quickly. Most of my time was spent on industry and news research on topics like the private health insurance death spiral and pricing of mortgage broker trailing commissions. Not the most glamorous work, but it gave me a window into how actuaries frame and contextualise problems before the numbers even come into it.

Overall, I found it interesting, but I also came away with a sense that I wanted something broader. Traditional actuarial roles tend to sit within financial services, but I was curious about what these skills could look like applied elsewhere more broadly. Data science felt like the natural path to that. In other words, a way to carry the analytical foundation into industries and problems that actuaries hadn’t traditionally touched. Joining Quantium after graduating was a deliberate choice with that in mind and it’s a decision I haven’t looked back on since.

What has helped you most in building and progressing your career?

1. Staying current with the latest technology. The landscape has shifted dramatically in the five years since I started. I have made a point of learning continuously, whether that is building things outside of work, reading widely and taking on projects that push me into unfamiliar territory. That habit has compounded over time.
2. Mentorship in both directions. I have been fortunate enough to work with inspiring leaders who are not only technically brilliant but also have incredible business acumen. On the flip side, mentoring junior colleagues has sharpened my own thinking enormously. When you have to explain a topic or task clearly to someone else, you quickly discover the gaps in your own understanding.
3. Willingness to volunteer for projects that scared me. There are a few projects in the past that I wasn’t entirely sure I could deliver on due to their challenging and ambiguous nature, but they turned out to be the ones that have grown me the most professionally because it forced me to always be on my toes and to upskill drastically.

Where has your career taken you — any roles, projects, or locations that surprised or shaped you?

The project that surprised me most was scaling an AI application from a small proof-of-concept to a full enterprise system across Australia, New Zealand and Asia in just six months. I knew it would be technically complex, but I underestimated the organisational complexity: different data standards, different infrastructure requirements, different stakeholder expectations across regions. Getting that right required as much communication and relationship-building as it did engineering.

I have also been shaped by the COVID-19 dashboarding work early in my career. There’s something clarifying about knowing that the numbers you’re producing are being used in real decisions about hospital capacity and resource allocation. It made me take data quality very seriously, a habit that has served me well ever since.

How has technology or data changed the way you work  — and how are you responding to that?

The pace of change is probably the most striking thing. When I started, the toolkit was R, SQL, Python, and fairly classical statistical methods. Now I’m working with large language models, prompt engineering, observability platforms and cloud infrastructure. That’s a significant shift in a relatively short period of time.

What’s changed most in my day-to-day life is how I actually build things. AI-assisted coding tools like Claude Code have made me meaningfully faster. What used to take a day can take an hour, but that speed only helps if you have the judgement to know when the output is right and when it isn’t. If anything, it raises the bar on understanding, not lowers it.

My response to all of this has been to stay genuinely curious and eager to learn. I try to understand new tools at a level deeper than surface usage, for instance, how they work, where they break, what they’re assuming. That instinct comes directly from actuarial training, and it’s the thing I find most transferable as the technology keeps shifting underneath me.

What advantages does being an actuary give you in the professional world?

Credibility and rigour. When I walk into any conversation about model uncertainty or data quality, having actuarial credentials means people take the concern seriously. There is a long track record of actuaries getting things right and that reputation is an asset.

Beyond the credential, the actuarial training gives you a genuinely useful mental model for working with uncertainty. A lot of data science and AI work involves making probabilistic judgements under incomplete information which is exactly the domain actuaries are trained for. I find I’m often the person in the room asking what are we assuming here? Where could this model fail? What is our confidence interval on this estimate? Those questions matter enormously in production AI systems and they come naturally from an actuarial education.

How do you explain the value of actuarial thinking to people outside the profession? 

Actuaries are trained to be professionally wrong in a useful way. We build models, we know they are simplifications of reality and we are rigorous about quantifying how wrong they might be and in which direction. That’s very different from just producing an answer.

In the current AI age, this is the difference between a model that generates predictions and a model that you can actually trust. Getting to the second one requires exactly the kind of systematic, adversarial thinking that actuarial training instils. Most people recognise that immediately, especially after they have been burned by a model that looked great in testing and failed badly in production.

What are the top five skills  — technical or otherwise  — you think every actuary needs today?

1. Programming: The specific programming language matters less than the ability to think computationally.
2. Intellectual curiosity: The field moves fast and constantly changes. The actuaries who thrive are the ones who find that exciting rather than threatening.
3. AI fluency: Understanding not just how to use AI tools, but how they work, where they fail, and what assumptions are baked in.
4. Scepticism: Healthy, productive scepticism about your own results. Always ask what would have to be true for this to be wrong?
5. Communication: The ability to translate a complex model into a decision a non-technical stakeholder can act on.

What are the three highlights of your career so far?

1. COVID-19 project with the Department of Health. I was only six months into my career and it was my first taste of high-stakes analytics, knowing that the work was being used in decisions that impacted people’s lives made it incredibly meaningful.
2. Working with cross-functional and cross-border teams to help a UK retailer optimise their promotional strategy. Collaborating across teams and time zones and seeing our models translate into measurable improvements for the client was a reminder of how much more you can achieve when different people bring their own unique skillsets and experiences.
3. Developing one of the company’s first AI applications. I came into it with no prior AI experience. I was learning the technology as I built it, but it was the project that got me from knowing nothing to now spending all my time thinking about where AI is heading and how it can be used to solve real client problems. The trajectory started there.

How would you describe yourself in one sentence? 

A data scientist turned AI engineer who thinks like an actuary: rigorous about uncertainty, curious about new technologies and always interested in work that genuinely matters.

What advice would you give to someone just starting out in actuarial studies or their first role?

Take the exams seriously, but don’t let them consume your entire professional identity. The actuaries I admire the most are the ones who bring genuine curiosity and human judgement to their work, not just technical competence.

Get comfortable with technology early. The profession is changing rapidly, especially in today’s age with the rise of AI. The good news is that these skills compound: every project teaches you something that makes the next one easier.

Find people whose work you respect and ask them questions. The actuarial community in Australia is small and genuinely generous. Most people are willing to share what they know and how they get to where they are. Don’t wait until you feel ready, because nobody ever does.

Interested in your own actuarial qualification journey?

Explore the Actuaries Institute's qualification pathway and find the program that's right for you.