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.
Join our dynamic team at Geneva Trading as a Quantitative Researcher, where you'll be at the forefront of shaping our trading strategies and driving our continued success. As a pivotal member of our team, you'll engage in every facet of strategy implementation, from initial ideation to seamless integration into our operational framework.
Responsibilities
- Innovating feature engineering processes to build a maintainable and scalable feature store
- Spearheading model training and selection endeavors.
- Seamlessly integrating models with our cutting-edge proprietary software.
- Fostering collaborative partnerships with our accomplished trading team to translate trader insights into actionable features.
- Continuously monitoring model performance and fine-tuning as necessary.
Requirements
- Demonstrated proficiency with 3 or more years of hands-on experience implementing machine learning solutions in a real-world trading environment.
- Educational background holding a Bachelor's, Master's, or Ph.D. degree in a technical discipline such as Mathematics, Statistics, Computer Science, or related fields.
- Expertise in at least one programming language of choice.
- This role is open in our Chicago, Dublin, and London offices
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We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
