ML Quant Researcher

Role Overview

Key Responsibilities

• Own the full research lifecycle, including hypothesis generation, experiment design, model validation, risk controls, and production deployment
• Conduct advanced research in machine learning and AI, applying state-of-the-art techniques (e.g. deep learning, transformers, representation learning) to quantitative finance problems
• Develop and deploy models that have a measurable impact on trading performance in options markets
• Rapidly prototype, test, and iterate on ideas, moving efficiently from concept to production
• Identify alpha opportunities within large, complex, and high-dimensional datasets
• Build scalable research pipelines, including feature engineering, distributed training, and backtesting systems
• Collaborate closely with trading and engineering teams to translate research insights into executable strategies
• Leverage extensive compute and data resources to run ambitious experiments at scale
• Continuously refine models and strategies based on live performance and evolving market conditions

Skills and Experience

• Advanced academic background (Master's or PhD) in Mathematics, Statistics, Physics, Computer Science, or a related quantitative discipline
• Strong research experience in machine learning, AI, or statistical modelling, with evidence of solving complex, real-world problems
• Deep understanding of modern machine learning architectures and training methodologies, including large-scale models
• Experience with training techniques such as pre-training, fine-tuning, reinforcement learning, and optimisation methods
• Proven ability to take models from concept through to production with measurable impact
• Strong mathematical foundations, including linear algebra, probability, and optimisation
• Fluency in Python and experience with relevant libraries (e.g. NumPy, PyTorch), with the ability to write clean, scalable code
• Hands-on experience with modern ML approaches such as sequence models, transformers, regularisation techniques, and robust validation methods
• Experience working with large-scale datasets and distributed systems
• A practical, results-oriented mindset with a bias for action and rapid iteration

Personal Attributes

• Highly curious, with a strong interest in financial markets and a desire to build domain expertise in options and market microstructure
• Comfortable operating in a fast-paced, high-performance environment with significant ownership and autonomy
• Strong problem-solving skills and the ability to simplify complex challenges into actionable solutions
• Collaborative mindset, with the ability to engage effectively with both technical and non-technical stakeholders
• Motivated by impact, with a clear focus on delivering measurable results

What Sets This Opportunity Apart

• Direct link between your work and trading outcomes, with clear feedback loops on performance
• Access to large-scale data and significant computational resources
• A flat structure that encourages ownership, speed, and entrepreneurial thinking
• The ability to work on cutting-edge machine learning problems with real-world financial impact
• A highly collaborative and intellectually rigorous environment