I caught up with Marcus Ahmad, who recently joined Squarepoint Capital, to discuss the start of his career in quant research

Marcus started working at Squarepoint in August 2023, having applied to the company through Bowden Brown.

Growing up in the US, he moved over to the UK in 2016 completing his A-Levels and going on to do his Undergraduate and Master's degree in Mathematics at Cambridge University.

We had a brief chat around his path into Quant Research , how he's finding the work so far and the realities of transitioning from academia into industry

When did you start to consider quant research as a career path?

MA - I was interested in finance after I started following the US stock market and doing some retail investing during the pandemic. I really liked trying to identify basic patterns in the price graphs to see if I could ‘time’ the market, but it was never a super scientific process. At University, one of my friends had done a quant trading internship and explained there’s a whole industry that approaches these types of problems systematically. I applied for a quant trading internship myself, and during that internship I was fortunate enough to have the opportunity to do a 3-week research project. While my statistics knowledge and coding ability weren’t quite strong enough at the time to achieve a real output in the amount of time I had, I loved the style of problem-solving I was doing which drew upon intuitions from my degree but applied to real-world problems.

How have you found the first few months settling in? Has anything surprised you?

MA - I was surprised how quickly I was doing real work for the firm. By the end of the first week, I was reviewing existing strategies for the team, so it was great to know I was making a real difference from the get-go. The first few months can be overwhelming, particularly because there will inevitably be gaps in your knowledge that you are expected to fill independently in order to get your work done. There is great support and training, but you do need to be proactive to make sure you’re learning everything you need to since you no longer have the structured learning you get at University. Make sure you’re comfortable explaining your thinking, asking for help, and hearing how your thinking may not always be correct…

What’s your day-to-day like?

MA - I spend most of my day researching new signals. This includes importing and cleaning the data, reviewing statistical properties of the data, and testing different signal constructions from the data to predict price movements. I'm also involved in monetizing these strategies, which includes testing strategies based on the signal under more realistic market constraints. An important part of this process is collaborating with other teams to identify new potential signals and to understand better why the existing signals work.

What’s the work / life balance like?

MA - Work / life balance can vary depending on the particular firm but tends to be quite good in the quant industry. Researchers in particular aren’t necessarily constrained by market open and close, so we have a bit more freedom with our hours. At the end of the day, it is our responsibility to get our work done, but beyond that, we get flexibility to schedule the day how we like. I think most firms in the industry realize they will get better output from happy employees than from overworked employees. As far as exact numbers, I probably average about ~48 hours on a normal week.

What’s the biggest difference between university research and quant research?

MA - My degree was quite theoretical, and I didn’t even really work with real data until my masters course, where I was still only doing so in 1 or 2 courses. At the degree-level, the emphasis tends to be on scientific rigor whereas in my career, I need to determine the right balance between rigor and practicality. I’ve found the ability I developed in my degree to think about problems in a thorough and precise way forms a really good foundation for how I approach real-world problems, but it is equally as important to understand that real-world problems often require unifying practical assumptions with these purely mathematical intuitions. It’s been great to see how the concepts I learned in my degree can be used as tools, rather than out-of-the-box solutions, for developing good trading strategies. Overall, applying these ideas in finance has tested my fluency with the concepts, especially understanding the heuristic value of the theory beyond the detailed logic that was used to derive it in the first place.

What interview advice / preparation would you recommend to aspiring quants?

MA - It really depends on what type of quant you want to become! Traders need to be able to think on their feet and make quick, precise decisions that have some quantitative basis. Any strategy-based game is great practice for the style of thinking you might be tested on in interviews -- poker is really common for traders, since it involves probabilistic decision-making that traders use in their day-to-day job to make trades. You’ll also be expected to be fluent with your mental maths, and ZetaMac is great for practicing that. For researchers, there is more of an emphasis on strong statistical and coding knowledge. You’ll probably be required to complete a coding test as part of the application process, and websites like HackerRank are a great way of prepping for this stage. Kaggle also offers really relevant projects that give you the opportunity to work with real data in the same way you’d do on the job -- doing one of these projects will give you a lot to talk about in your interviews. However, both traders and researchers are expected to have strong probability problem-solving abilities. The best way to prepare for this side of the interviews is to just do problems. There are loads of interview prep books for this, but my two favorites were ‘A Practical Guide to Quantitative Finance Interviews’ by Xinfeng Zhou if you’re looking for a more structured introduction to the style of thinking, whereas ‘Probability and Stochastic Calculus Quant Interview Questions’ by Matic, Radoicic, and Stefanica is great if you want to stretch yourself.

Is there anything you would have liked to have known before going into QR?

MA - There’s a huge software component, so if you’re like me and from a mathematical background, it can be really helpful to get some experience working with things like Git and object-oriented programming.

Massively appreciate Marcus finding some time to chat with me and share his experience of working within the quant finance so far and more generally his journey towards this career path.

Some really useful and illuminating insights into an industry notorious for it's opaqueness - with alot of this information being particularly useful for those considering Quant Research as a career path and preparing for upcoming interviews.

If you're thinking about a career in quantitative finance, feel free to get in touch. It would be great to discuss possible routes and share ways to ensure you're in the best position to succeed.