Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.

At Susquehanna, our quantitative systematic traders represent a true hybrid of our core disciplines: trading, technology, and quantitative research. Drawing on their technical proficiency, research experience, and understanding of trading, quantitative systematic traders’ work involves identifying trading signals, developing strategies, and collaborating extensively with members of the trading, quantitative research, and technology teams.

As a graduate on our quantitative systematic trading programme, you will benefit from our industry-leading education programme run by a team of senior traders and quantitative researchers. Don’t worry if you don’t come from a finance background – we will teach you everything you need to succeed.

A core strength of our graduate programme is that our classroom-based education is complemented by hands-on trading simulations and project work. Graduates are immersed in real-world algorithmic trading problems, learning about our proprietary dataspaces, novel modelling approaches, and strategy implementation. Additionally, quantitative systematic trading graduates develop a deep understanding of trading decision-making by participating in various types of trading simulations.

Research-minded: You enjoy diving deep into a question and building mathematical models to reveal patterns in vast amounts of data.

Technically proficient: You have experience writing code to process and analyse large datasets.

Clear communicator: You enjoy building relationships with those you work closely with and are comfortable sharing ideas, expressing your thoughts, and listening to the views of others.

Ambitious: You’re someone that enjoys setting personal goals and giving your all to achieve them. Be it in Arts, Sports, Academics, or another passion, you’re not afraid to put in the effort to reach your full potential.

Soon-to-be master’s/PhD graduate or post-doc: You conduct research in a quantitative discipline and are available to join the programme in June 2026.

What’s in it for you:

Education: Our industry-leading education programme is designed to facilitate your transition to the world of quantitative trading. Classes on various topics (Options Pricing, Machine Learning, Data Analysis Techniques, Game Theory, Trader Talks, In-house trading games… the list goes on!) will be complemented by hands-on project work and trading simulations.

Collaboration: You will get to work with a wide range of people in the classroom and through your project work; people who share a passion for mathematics, problem-solving, game theory, and technology.

Culture: Our non-hierarchical, casual, and collaborative culture allows employees of every level to thrive.

Perks: We offer a highly competitive compensation package to our graduates. Located in Ireland’s Financial Services Centre, in the heart of Dublin’s city centre, our office features free onsite catering, a games room, and a fully equipped in-house gym.

Susquehanna is an equal opportunity employer, and we are committed to diversity and inclusion throughout our recruitment process. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruitment process, please contact us.

If you're a recruiting agency and want to partner with us, please reach out to recruiting@sig.com. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.

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