Quantitative Researcher (Machine Learning)

Quantitative Researcher (Machine Learning)

We are seeking a talented Quantitative Researcher to join our competitive global quantitative trading team at Geneva Trading. In this role, you’ll research, develop, and deploy automated intraday and mid-frequency trading strategies using machine learning models and advanced quantitative methods. You’ll work with large datasets, applying statistical techniques to drive real-time trading decisions.

As part of a lean, skilled team, you will contribute across the entire pipeline, from data preprocessing to model deployment, ensuring the integration of research and real-time execution. This hands-on role combines quantitative research with software engineering, requiring strong coding abilities and the application of CI/CD, DevOps, and MLOps principles.

Key Responsibilities:

• Design and execute research experiments to develop innovative models and strategies, evaluating results rigorously.
• Develop production-ready code for live trading integration, collaborating with developers.
• Enhance research and trading infrastructure through machine learning methods, including data preprocessing, feature selection, model training, and backtesting.
• Monitor live trading strategies for performance issues such as covariate shift.
• Integrate external libraries into production code following best engineering practices.
• Optimize model training and backtesting using parallel, distributed, and cloud computing.
• Explore opportunities for strategy expansion across global futures products.
• Stay current with industry advancements through research, competitions, and online communities.

Required Qualifications:

• Academic Background : Master’s or PhD in a STEM field (e.g., Machine Learning, Computer Science, Physics).
• Experience : 2+ years of applied machine learning experience in a commercial or academic setting, or 2+ years in quantitative research or development in trading.
• Skills :

• Strong understanding of multivariate statistics, time-series analysis, machine learning, and optimization.
• Strong programming skills in Python, including libraries like NumPy, Pandas, and Scikit-learn.
• Familiarity with Q/KDB and Git.
• Strong mathematical ability in linear algebra and calculus.

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