An Interview with a Quantitative Risk Analyst

Tell us about the work you do in your current role?

I turn complex data into decisions that matter. At Ninety One, I work in quantitative risk management within fixed income. My focus is on stress testing, automation, and building systems that help us understand and manage risk across portfolios. This involves developing frameworks to model how portfolios behave under different scenarios, and creating tools that make these processes faster, more consistent, and easier to use. Much of my work is about designing robust systems that scale - whether that’s automating risk workflows or building applications that integrate analytics directly into decision-making.

Before joining Ninety One, I worked at a systematic investment manager where my role sat at the crossroads of quantitative research, investment strategy, and technology. There I built models and tools for traders and portfolio managers - everything from natural language models to interpret central bank speeches, to economic nowcasting systems, to dashboards and APIs that brought our research to life. That experience taught me the value of making advanced quantitative ideas practical and usable in day-to-day investment decisions.

In short, my career has been about combining quantitative methods, data science, and technology to turn good ideas into practical systems - and now, at Ninety One, applying that approach to risk management in fixed income.

Where and in what role did you start your career and how did you get to where you are today? 

I was born and raised in Cape Town and, like many overly curious 18-year-olds, decided that statistics and investment management sounded like a fun combination. I studied at Stellenbosch University, where I learned how to run regressions, calculate Sharpe ratios, and survive on instant coffee and exam panic.

After undergrad, I took the logical next step: more math. I completed my master’s in risk management of financial markets at the University of Cape Town through a program run with some of South Africa’s biggest banks. It was 2020 (peak COVID) and everything felt like a risk. So naturally, I studied it.

Originally, I thought I’d starting risk management, quietly stress testing portfolios and explaining fat tails to whoever would listen. However, I then joined a graduate program in asset management which widened my view, and I found myself drawn to building things, such as models, tools, or engines, that could help manage portfolios in real time. Now in almost a full circle, I use those skills the risk space.

I later pursued the CQF, partly to sharpen my quantitative skills and partly because I wanted to truly understand the formulas I’d been using and get to grips with the financial engineering behind them. It gave me the depth I was looking for, and a much stronger foundation for building real-world models and tools.

Looking back, the turning point was realizing I didn’t just want to measure risk. I wanted to model it, automate it, and build with it, whilst still trying to stay one step ahead.

What are some of the highlights and the biggest challenges?

For me, the biggest highlight - and also the biggest challenge - has been the sheer breadth and dynamism of the work. Over my career I’ve moved between data science, quantitative finance, derivatives, sentiment analysis, and now stress testing and risk management. Each shift has opened new opportunities, but it’s also forced me to adapt quickly and build new skills.

A good example is how I’ve gone from building models to extract trading signals from central bank speeches, to designing derivative strategies, to now automating stress testing frameworks that help us understand fixed income portfolios under different scenarios. The common thread is taking something complex and turning it into a practical system that informs real decisions.

The challenge is that the complexity never goes away. Whether it’s separating signal from noise in unstructured datasets or ensuring that a stress testing model holds up in real markets, the hard part is always making sure what you build is both rigorous and usable. But that’s also the rewarding part - finding ways to bridge theory and practice, and seeing your work directly shape how risks are understood and managed.

Please may you briefly describe what a typical working day looks like in your current role?

I read the news, particularly geopolitics. It mostly feels like watching a real-life soap opera, except the plot twists come with real consequences for markets.

We usually start the day with a quick team huddle to go over what we’ve done and what’s still on the table. Then my boss an I (also, a CQF Alumni) and I head out for a brisk coffee walk - part ritual and part rolling whiteboard session - where we bounce around ideas, troubleshoot problems, and sketch out what we’ll tackle for the day. There’s always a bit of banter and joking mixed in too, which keeps things light even when the work is intense.

Once we’re back, it’s straight into the data and models, for example running simulations, updating volatility surfaces, refining sentiment signals, or digging into whatever project needs attention. Some days are about building, others about questioning. Either way, the goal is the same: turn information into insight and insight into action.

It’s a mix of research, development, and decision support. Some days are more technical - writing code or debugging pipelines - while others are focused on interpreting results, presenting insights, or figuring out what story the data is trying to tell. No two days are quite the same, which is probably why I like it.

You earned the Certificate in Quantitative Finance (CQF). Why did you decide to enroll in the program and where has the CQF added value to your career?

I didn’t want three letters just for the sake of it. I wanted something that would sharpen my thinking and give me tools I could use day-to-day. That’s why I chose the CQF.

Coming from a background in risk and investment management, I was already familiar with financial theory, but I wanted to go deeper into the practical side. The CQF offered that hands-on approach, and that’s what drew me in.

It’s added real value to my career. I’ve applied what I learned in building backtesting engines, developing option pricing models, and creating tools that connect research to execution. More than anything, it helped me bridge the gap between theory and practice, which is exactly where I like to work.

What do you think are the most important skills for professionals in your field to have?

In my field, the most important skills are a blend of technical depth, curiosity, and communication.

You need to be able to build, so the ability to take an idea and turn it into a working tool is essential. That means being fluent in programming (Python, R, SQL, JavaScript etc.), understanding financial concepts deeply (derivatives, pricing, risk), and being comfortable with data (cleaning it, interpreting it, and extracting meaning from it).

It’s not all about just technical skills though - you also need good judgment. You need to know when a signal is noise, when a model is overfitting, or when a solution is elegant versus overly complex. You also need to be able to explain what you’ve built clearly to people who may not share your background.

If someone is new to the industry, I’d say start with projects. Build things that interest you. Recreate models you read about. Write code, break things, and fix them. Learn by doing but always ask why something works and not just how. Read papers, follow the markets, and stay curious. The best people I know in this field are the ones who never really stop learning.

Do you think the industry has changed since you started your career, and how do you see it changing in the next few years?

Absolutely. The industry has changed a lot since I started, and it's still shifting fast.

When I first stepped into the world of finance, there was already a push toward data-driven decision-making, but it was still mostly siloed (i.e., quant teams over here, PMs over there, tech in the background). Now, those lines are blurring. Data science isn’t just a support function anymore. Now, it’s part of the investment process. Models are more integrated, infrastructure is more scalable, and tools are being built with the end-user in mind.

In the next few years, I think we’ll see even more convergence between finance and tech. Natural language processing, alternative data, and machine learning will continue to gain ground as practical tools in portfolio construction and risk management. There’s also a growing emphasis on transparency and explainability, especially with AI in the mix.

At the same time, I think the human element will become even more important. As automation increases, the ability to ask the right questions, interpret model behavior, and apply sound judgment will be what sets people apart. So yes, the tools are changing, but the core challenge remains the same: making smart decisions in a complex, uncertain world.

What would your advice be to someone starting a career in your field today?

First off, if you're thinking about taking a gap year, do it. Travel, build something random, surf for a while, read books that have nothing to do with finance. It’s not wasted time. In fact, some of the most useful insights I’ve had come from not staring at a spreadsheet.

A career in this field can be incredibly rewarding, but it’s also demanding. So don’t just chase titles or trends. Your job is to find what part of this space actually excites you. Whether it’s solving puzzles in data, understanding markets, building models, or finding patterns where others see noise - you need to lean into that.

Remember, creativity has a place. It’s easy to think of quant finance or data science as rigid, technical fields, but they’re not. They're about asking good questions, designing clever solutions, and telling stories with data. So, use your life experience, interests, travels, or side projects as fuel for understanding people, behavior, and risk.

In the end, this isn’t just about having a job. It’s about building a career that challenges you, teaches you, and keeps you curious. That’s where the real reward is.