Quantitative Developer – Risk - Trading Desk
Lead Financial Services Firm
Hybrid On-Site
Total Comp: Base + Cash Bonus - Advertised Basic Salary Only.
The Opportunity
A leading global financial services is seeking an experienced Quantitative Developer to build sophisticated risk infrastructure and quantitative analytics platforms supporting its equity derivatives trading desk.
We are particularly interested in developers who combine strong Computer Science fundamentals with a deep understanding of quantitative risk methodologies and production-grade engineering.
Key Responsibilities
- Design and develop scalable risk analytics platforms and quantitative infrastructure using Python.
- Build and enhance frameworks supporting equity factor models, portfolio exposures, risk attribution and performance analytics.
- Implement quantitative methodologies for factor decomposition, covariance estimation and portfolio risk measurement.
- Engineer high-performance systems capable of processing large volumes of market, security and portfolio data.
- Develop robust Python libraries, APIs and distributed calculation frameworks supporting quantitative risk models.
- Work closely with Risk Quants to translate statistical models and research methodologies into production-ready applications.
- Optimise computational efficiency, memory usage and processing performance across complex analytical workloads.
- Contribute to software architecture, data pipelines and engineering standards across the risk technology platform.
Requirements
- 5+ years of professional Quantitative Development or Software Engineering experience within financial markets.
- Advanced Python software engineering expertise, including object-oriented design, algorithms, data structures and application architecture.
- Proven experience building production-grade Python applications, libraries or quantitative platforms — not simply Python scripting or data analysis.
- Strong understanding of equity factor risk models, including their construction, implementation and practical application.
- Experience with portfolio risk analytics, factor exposures, risk attribution or quantitative modelling.
- Strong knowledge of SQL, data engineering and large-scale financial datasets.
- Understanding of distributed computing, performance optimisation and scalable backend systems.
- Academic background in Computer Science, Mathematics, Statistics, Physics, Engineering or a related quantitative discipline.
- Ability to collaborate effectively with Quantitative Researchers, Risk professionals and Software Engineers.
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