About the Company
You'll join a small, London based Financial Risk team that designs, develops and deploys risk models covering credit, market, capital and liquidity for derivative trading. The group works closely with Model Validation, Regulatory Capital, Finance, Treasury, Credit Operations, Enterprise Data and Technology. The environment is cross-functional, commercially focused, and hybrid (typically three days on site).
About the Role
Help build, maintain and productionise quantitative risk models and the surrounding automation so the business can act quickly on high-quality risk insights. You'll contribute code, testing, documentation and operational run-books, and support incremental migrations toward cloud tooling.
Responsibilities
* Contribute to the design, development and deployment of Python-based risk models (e.g., components of VaR/ES or PD/LGD pipelines) under senior guidance.
* Refactor and harden existing code paths; add unit tests, data validation checks and logging.
* Build and maintain CI/CD jobs in GitLab for model rebuilds and scheduled tasks; assist with release notes and rollbacks.
* Automate recurring processes and controls (data loads, reconciliations, report generation).
* Collaborate with first-line commercial teams to clarify requirements and triage model output questions.
* Support early cloud migration tasks (e.g., packaging jobs, testing connectors, basic Looker dashboards) with mentorship from seniors.
Requirements
* Experience with Python (in line with Associate/AVP level) in a data or risk/quant developer or adjacent role, including pandas/NumPy and writing testable, readable code.
* Solid SQL for data wrangling and reconciliation.
* Practical Git experience; familiarity with GitLab or similar CI/CD.
* Understanding of at least one risk domain (market, credit, capital or liquidity) and basic knowledge of common models (e.g., VaR/ES or PD/LGD).
* Comfortable working in a hybrid, collaborative setup with clear, concise communication skills.
Preferred Skills
* Experience in a bank/consultancy risk team or adjacent regulated environment.
* Exposure to MongoDB, GCP, Looker, or Vertex AI.
* Familiarity with model monitoring/alerting and data quality frameworks.