Quant Trader- Dublin

Founded in 1999, Geneva Trading is a premier global principal trading firm with strategically located offices in Chicago, Dublin, and London. Our relentless focus on trading excellence combined with technological innovation has equipped us with a best-in-class proprietary trading platform, enabling us to compete at the highest levels in the global markets. Rooted in a culture of integrity, collaboration, and an unwavering passion for progress, we foster an environment of personal and professional excellence. Our nimble organizational structure and entrepreneurial spirit attract top-tier talent with a passion for innovation, laying the foundation and driving our consistent success in the industry.

We are looking for Quant Traders to join our growing market-making team in Dublin. The ideal candidate has experience working at a proprietary trading firm and is looking for a position with more autonomy and the opportunity to assist with the rapid growth of a profitable trading desk.

How You Can Make An Impact:

• Managing positions and capitalize on trading opportunities
• Monitor and troubleshoot our proprietary software and trading infrastructure
• Analyze and model trading opportunities using real-world data.
• Build and enhance tools that drive trading decisions and risk management.
• Collaborate with traders and technologists to solve complex, high-impact problems.

What We’re Looking For:

• Strong programming skills in  Python  (or similar languages).
• Solid understanding of  statistics ,  probability , and  optimization  techniques.
• Exposure to  data analysis  tools (e.g., R, MATLAB, Pandas, NumPy).
• Curiosity about  financial markets ,  algorithmic trading , and  market microstructure .
• Ability to think critically and communicate complex ideas clearly.
• Ability to work in office.

Preferred Extras

• Experience with  kdb+/q , SQL, or other database technologies.
• Participation in  trading competitions ,  math contests ,  competitive gaming , or  poker .
• Familiarity with  machine learning  techniques such as regression, neural networks, or SVMs.