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Quantitative Researcher shares his path to DRW

We sat down with Quantitative Researcher Ben to discuss his career, the skills needed to become a successful Quant researcher and what he enjoys the most about the London office.

What was your path to DRW?

Before joining DRW, I was studying pure Maths, first as an undergraduate at Oxford University and then as a PhD student in Freiburg, Germany. I was mainly working in algebra and representation theory and didn't have much real-world experience with statistics or programming. I wanted to change this and started looking for jobs where I could work alongside other people from technical backgrounds on interesting problems, and market making seemed an obvious choice since I knew other mathematicians working in similar roles.

DRW is one of the largest market making firms, so it was a natural decision to apply. I was extremely impressed by the atmosphere in the office when I interviewed and was pleased to start working here three years ago.

Please describe your current role and day-to-day responsibilities and projects?

I am a Quantitative researcher on the Equity Index Options team and spend most of my time researching our systematic low-latency trades. This is an area where data driven decision making is crucial and small optimisations of a strategy can be the difference between making and losing money.

What is your favourite part of your job? What is the most challenging part of your job?

Each project cycle typically lasts for one to two months, delivering and seeing new research results being used is exciting. On the other hand, at the start of a new project it can be tricky to think through all the potential issues each approach could cause further down the line. Making bad decisions early on in a project can mean a lot of time spent correcting those mistakes.

What 3 skills are key for someone interested in a Quantitative Researcher role at DRW?

  1. It's important to have some understanding of how to program a computer and the fundamentals of a scientific programming language such as Python.
  1. Basic knowledge of statistics, such as linear regression is important as this is the foundation of data driven modelling and machine learning.
  1. The ability to explain results effectively to people from diverse backgrounds. Quants at DRW work closely with developers and traders, who all have different perspectives about how things work.

What's your favourite thing about the London office?

The office always has a lively atmosphere and there's an openness to sharing knowledge across and within teams. It's easier to learn from each other in person, which is one of the reasons working from the office has been a popular choice since it reopened after lockdowns. Apart from that, there's plenty of free food, including a choice of lunch each day.

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