What first attracted you to Optiver?

I wanted to try a career in quantitative finance, since it’s one of the few options where I can utilise my background in both mathematics and computer science. I first found out about Optiver when my friends were discussing their challenging interviews. I was especially intrigued by the unique strategic betting questions, so I decided to take up the challenge and apply for a role at Optiver.

What does your role entail?

Working in the Delta 1 (D1) team as a quantitative researcher involves handling a lot of noisy data, making sense of it, and extracting meaningful signals to inform automated trading strategies. The role is really interesting because it’s a great mix of data analysis and programming, and there are many areas for a researcher to contribute, whether it’s improving the data pipeline or developing better strategies.

What are the key skills required?

Fundamentally, the ability to think about unfamiliar problems and persist through them is required. Python programming is heavily utilised for data processing and analysis. Other essential skills include initiative, attention to detail, and curiosity to learn.

What was your training like?

The bulk of my training happens on the job when communicating with my team, reading wiki pages, and understanding existing codebases. Other than that, I had the opportunity to attend a two-week training program that included options theory, simulated trading, and an overview of the whole business. There is also no barrier to asking random questions around the office, in order to learn more about what goes on beyond your team.

What are some of the projects you’re working on (non-confidential)?

The projects I work on involve examining historical data to improve D1 strategies. I performed research to measure slippage and analyse how closely our backtest results matched with the outcomes in production. Currently, I’m processing data to refine our research pipeline.

How do you start your day at Optiver?

The pantry here is well stocked. In my past mornings, I’ve had milo, cereal, fruits, hard boiled eggs, avocados, sandwiches, and the list goes on. The unchanging constants are that I get myself a refreshing glass of sparkling water (which I’m not sure how I’m going to live without after this internship), and I plan my priorities for the day in my notebook.

What do you love about your work?

I love that the work lies at the intersection of mathematics and computer science, which is totally in line with what I was hoping for. Not only that, Optiver emphasises having a connection to the actual trading business, so I get a lot of exposure and there is a strong focus on how my skills and tasks contribute to the bottom line, which is very rewarding. The people here are also super interesting, approachable, and happy to help, which is invaluable for any internship.

What keeps you motivated?

I’m enjoying the opportunity to consider challenging problems, chip away at them with my technical skillset, and benefit the strategies, all while getting the opportunity to learn about mathematical and trading concepts. It’s also rewarding to know that my work makes a direct impact on my team. Throughout the internship, I’ve discovered many avenues I could pursue to add more value to my team, and I really wish I had the time to come back and get through all of them!

What interests outside of work help you in your role?

Recently, I developed an interest in strength training, which gives me a sense of achievement and helps me relax and reset mentally at the end of a long day. Other than that, I love interacting with animals and anything related to art and design. Pretty unrelated to my career interests, but not “working” all the time helps me prevent burnout at work.

What’s your advice to potential applicants?

Behaviourally, make sure you know yourself well and prepare yourself by finding out more about Optiver so you can clearly communicate what interests you about the role. Be enthusiastic and genuine, the interviewers are invested in getting to know you and mine even transferred me to a role they thought would better suit me afterwards (they were right about that too).

Technically, refresh your problem-solving skills, be curious, and don’t give up when you don’t know something. Just take it one step at a time and you will get there in the end. If you’re applying for a research internship, comfort with Python data processing will help a lot.

I had a very positive experience during my interviews and didn’t face any kind of “stress tests” or “intimidation techniques”, even when I didn’t know something. Finance knowledge isn’t needed either, so if you’re curious, don’t be afraid to apply!