The Automated Trading Strategies (ATS) group is responsible for systematic trading across FX, Rates, Commodities, and Credit markets. The team is responsible for a broad scope including the design and implementing of cutting edge proprietary quantitative models that drive our automated trading systems (pricing, risk management and execution), the oversight of day-to-day risk and operations, and the optimization Franchise client liquidity offering in a data-driven manner.

Job Summary

As an Associate AI\ML Researcher within Automated Trading Strategies team, you will accelerate our efforts on applying latest AI\ML methods on systematic trading strategy R&D. As part of a small focused team with minimal bureaucracy, you will have great independence to pursue the research directions you think would be most impactful. You are enabled with enough computational resources, and supported by excellent data and engineering teams to realize your vision. Your work will be deployed directly into production trading with P&L responsibility. The field is complex and often requires creative problem-solving, but it’s also a great chance to learn and grow professionally.

You will be responsible for improving every part of our models: from featurization of data, to architecture design, to training dynamics, to how trading decisions are made. You will be part of a fast-growing effort and have the opportunity to have a holistic view of market making & exchange trading, including alpha generation, portfolio construction/optimization and trade execution algorithms. This position does not require prior finance knowledge or experience. Candidates with experience in technology, AI or research institutions are strongly encouraged to apply.

Job Responsibilities

• Apply AI\ML model to find predictive pattern from large dataset
• Integrate prediction model into existing strategies or setup new one
• Contribute to team’s AI\ML libraries as well as production algo system

Required qualifications, capabilities, and skills

• Experience in machine learning and deep learning research for any domain
• Relevant experience using frameworks such as Py Torch, Tensor Flow or equivalent
• Strong engineering skills valued, efficiency in other languages (Java/C++/C#) beyond Python
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward

This role encompasses the performance of UK regulated activity. The successful candidate will therefore be subject to meeting UK regulatory requirements in the assessment of fitness, propriety, knowledge and competence (as assessed by the Firm) and (where appropriate) approval by the UK Financial Conduct Authority and/or the Prudential Regulation Authority to carry out such activities.