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
FAQs
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