How to Become a Quantitative Trader: Insights from Susquehanna, Optiver and IMC

To become a quantitative trader, build strong mathematical reasoning, learn to make and explain decisions under uncertainty, and understand how trading teams use data and technology. Then choose an entry route that matches your stage of study and the actual role. A trader, quantitative researcher and software engineer can work on the same trading system while solving different problems.

That distinction comes through in CareerFairy recordings with Susquehanna, Optiver and IMC. The most recent, Susquehanna’s Life of an Options Trader recording from 3 November 2026, puts particular emphasis on pricing risk, understanding context and reviewing how decisions were made. It is a useful starting point if your picture of trading is mostly screens and fast mental arithmetic.

This guide uses those conversations to explain the work and suggest practical preparation. Employer examples describe the teams and programmes discussed in the recordings; check current advertisements for eligibility and selection details. For a wider comparison with other paths, see our finance career starter guide.

What does a quantitative trader actually do?

A quantitative trader uses mathematical models, market information and judgement to make or manage trading decisions. The precise balance between human decisions, research and automation varies by team.

In the November 2026 Susquehanna session, the speaker described traders as pricing risk and applying context to quantitative models. During quieter periods, that can mean reviewing earlier activity, discussing ideas with researchers and preparing for events. During busier periods, model inputs may be less certain, so communicating with other traders, researchers and technology colleagues becomes especially important.

Options are one example of the products involved. An option gives its holder a right, rather than an obligation, to buy or sell under specified terms. For a career decision, the important point is that the work involves evaluating uncertain outcomes and risk, rather than simply predicting whether a share price will rise.

Susquehanna’s speaker repeatedly returned to the limits of a model’s answer. What information might another market participant have? Has something changed that the model does not capture? A number is useful only when the team understands what produced it and when it might be misleading.

Trader, researcher or software engineer?

Look beyond a vacancy’s title to its responsibilities. The boundaries are not identical at every firm, and some roles sit between categories.

Quantitative trader: decisions and risk in context

At Susquehanna, the November 2026 explanation placed traders close to market activity, using models and knowledge of the market to evaluate decisions. The session also described quantitative systematic trading as a hybrid area between trading and research. That makes the job description more informative than a simple “trader versus coder” distinction.

In Optiver’s Career Kickstarter recording from 17 November 2025, traders described discussing overnight developments, challenging plans with colleagues and reviewing the day. Those accounts show collaborative judgement alongside quantitative work. They are individual desk experiences, not a universal daily timetable.

Quantitative researcher: test ideas with data

Susquehanna’s November 2026 session described researchers developing ideas, exploring data, modelling, backtesting and evaluating implementation. Backtesting means checking how an approach would have behaved on historical data. A promising historical result is a starting point for scrutiny, not proof that it will work in the future.

The speaker explained that a research idea can also come from a trader who notices a pattern. Researchers and developers then help establish whether the idea is meaningful and how it could be implemented. The work includes feedback and revision after a strategy reaches the market.

Software engineer: make the system work reliably

The IMC Zug recording from 15 October 2025 gives a different perspective, centred on high-frequency trading and the interaction of research, software and hardware. Speakers discussed research in Python and implementation of live strategies in C++. That describes the practice they presented in Zug at the time; it is not a coding rule for every trading job.

IMC’s speakers also used “researcher” and “trader” in overlapping ways. This is exactly why students should ask what a person builds, studies or decides, rather than assume that a title means the same thing across firms. If building dependable systems interests you most, compare this path with our junior software engineer expectations guide.

Build probability skills you can explain

Being quick with numbers can help, but preparation should also reveal your reasoning. Can you state assumptions, compare uncertain outcomes and change your conclusion when new information arrives?

The November 2026 Susquehanna session used a number-guessing game to show why a theoretically neat answer and a useful decision in a particular group can differ. The broader career lesson is to connect a model with the behaviour and information around it. Learning a formula without understanding its assumptions will not do that.

As an editorial practice exercise, take a simple probability problem and explain two approaches to a friend. State what is known, what is assumed and what would change the answer. Then ask your friend to challenge an assumption. This is preparation for clear reasoning, not an employer interview question.

Also practise separating a decision from its outcome. Susquehanna’s speaker explained that training simulations can contain randomness: a sound decision can have an unlucky result. Keep a short decision log for practice exercises, recording what you knew at the time and why you chose an approach. Review your process rather than celebrating every lucky success.

Use coding and projects as evidence

A small project you understand is more useful evidence than an impressive-looking system you cannot defend. Start with a question, use data responsibly and explain what you found, including limitations.

For a research-oriented project, you could investigate a public dataset and compare a simple baseline with another model. Keep the analysis reproducible. Explain how you separated development from evaluation and where the data could mislead you. This is an editorial project suggestion, not a claim that an employer requires a trading strategy portfolio.

For an engineering-oriented project, focus on tests, clarity and reliable behaviour. Be ready to explain why you chose a data structure, how you handled errors and what you would improve. For trading-oriented preparation, show how the analysis supports a decision and what uncertainty remains.

In the November 2026 recording, Susquehanna’s speaker was clear that using AI does not remove the need to understand model inputs and defend the result. If you use AI in a project, be able to explain and check the work yourself. Our guide to AI skills in graduate jobs explores that broader application challenge.

Compare insight, internship and graduate routes

These routes serve different purposes. An insight programme can help you understand the work. An internship lets you experience a team over a longer period. A graduate role is employment with its own training and responsibilities.

Susquehanna’s November 2026 session described discovery, internship and graduate routes aimed at different stages of university study. The current Susquehanna trading careers page also describes structured education and internships. Use the individual advertisement to check the relevant location, graduation window and role requirements.

Optiver’s five-day Career Kickstarter is an immersive programme, not a promise that five days make someone a fully trained professional trader. The November 2025 recording discussed selection for that programme, including mathematics, probability and logical reasoning. Do not assume its assessment sequence or dates apply to every Optiver graduate vacancy. Consult the official Career Kickstarter page for the programme currently described.

An internship is also a chance to learn which work suits you. Enjoying probability does not automatically mean you will prefer live market decisions to longer research projects. Enjoying programming does not automatically mean you want to spend your day developing models. Ask how much time a role spends on each activity.

Your practical preparation checklist

Use this to assess progress before you apply, rather than as a promise of selection.

• Role understanding: Explain how the advertised team uses models, data and technology, and where your role contributes.
• Mathematical reasoning: Practise probability and quantitative problems while stating assumptions and checking answers.
• Decision review: Describe a time you revised a view after new evidence or feedback.
• Technical evidence: Prepare one project you can explain in detail, including its errors and limitations.
• Communication: Practise presenting an approach and responding constructively when someone challenges it.
• Application fit: Verify the current graduation window, location, language and practical requirements for the exact vacancy.
• Questions for the team: Ask how new joiners are trained, how work is reviewed and how traders, researchers and engineers collaborate.

You do not need a story about always having wanted to trade. You need an informed reason for exploring this kind of problem-solving, supported by evidence of how you learn and work with others.

Start with the fresh Susquehanna recording, then compare it with Susquehanna’s November 2025 quantitative trading discussion, Optiver and IMC. Write down which activities appeal to you and which need further investigation. That will give you a stronger application direction than treating “quant” as one interchangeable career.