This role sits within a capital markets trading environment, building and quantitative models, pricing and risk analytics, and trading tools used across front office and related functions. You will work closely with traders, quantitative researchers, and technology teams to deliver robust, performant solutions across the trade lifecycle.
Key Responsibilities
- Design, implement, and optimise quantitative libraries for pricing, risk, and P&L explain, with production-grade quality.
- Develop low-latency and scalable components for pre-trade analytics, order management, and execution support.
- Integrate models into trading platforms, ensuring correct data handling, calibration, and numerical stability.
- Work with market data (real-time and end-of-day) and reference data; implement robust feeds and data quality checks.
- Partner with stakeholders to gather requirements, estimate work, and deliver enhancements under tight timelines.
Requirements
- Strong programming skills in Python.
- Solid understanding of software engineering fundamentals: algorithms, data structures, design patterns, multithreading, and performance tuning.
- Experience with capital markets products (e.g., equities, FX, rates, credit, derivatives) and trading workflows.
- Good grasp of quantitative methods: numerical methods, optimisation, probability/statistics, and time-series concepts.
- Experience with pricing and risk concepts (Greeks, VaR, sensitivities, curve construction, calibration) and model lifecycle.
- Hands-on with databases and data technologies (SQL; experience with time-series stores is beneficial).
- Familiarity with Linux, scripting, version control (Git), and build/CI tools.
- Strong communication skills, with the ability to work effectively with both technical and non-technical stakeholders.
- Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, Physics, or a related quantitative discipline.
- Experience with electronic trading, low-latency systems, and messaging technologies.
- Knowledge of model risk governance, testing frameworks, and production monitoring.