Machine Learning Researcher (Quant Researcher)

About the Role

We're partnered with a top-tier global trading firm that is investing heavily in building out a centralized machine learning ecosystem across its trading business. This is a unique opportunity to step into a leadership role at the intersection of ML research, trading, and infrastructure shaping how machine learning is applied at scale across multiple asset classes.

The role goes beyond pure modeling. You'll play a key role in defining the research platform, guiding experimentation, and enabling teams to deploy ML-driven strategies in production environments where performance and speed matter.

Responsibilities

• Lead the design, development, and deployment of machine learning models applied to trading and market prediction
• Drive research into advanced ML techniques for signal generation, forecasting, and portfolio optimization
• Partner closely with traders, researchers, and engineers to translate market intuition into data-driven models and features
• Oversee data pipelines, feature engineering, and integration of structured and alternative datasets
• Help build and scale a centralized ML research environment used across multiple teams
• Define best practices around experimentation, model validation, and productionization
• Mentor and guide junior researchers while fostering a strong research-driven culture

Requirements

• Advanced degree (PhD or Master's) in a quantitative field such as computer science, mathematics, statistics, or engineering
• 4+ years of experience developing and deploying applied machine learning models
• Experience working in performance-critical or real-time environments (trading experience is a plus, not a requirement)
• Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX or similar)
• Deep understanding of the theoretical foundations behind modern machine learning approaches
• Experience working across research and engineering teams to bring models into production
• Prior leadership or mentorship experience is a plus
• Strong communication skills and ability to operate in a highly collaborative environment

Why This Role

• Platform-level impact: Shape how machine learning is applied across an entire trading organization
• Blend of research + leadership: Stay hands-on while influencing broader technical direction
• Real-world impact: Work on problems where models directly translate to PnL
• Collaborative environment: Tight integration across trading, research, and engineering
• Build-mode opportunity: Help define infrastructure, not just use it