AVP – Python Quant Developer – Risk

About the team

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).

Help build, maintain and product ionise 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.

• 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 Git Lab 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.

• Python (pandas, Num Py; matplotlib for basic plots) and SQL for model development and analysis.

• Git Lab for source control and deployment pipelines.

• Exposure to Mongo
  
  DB and GCP services; growing use of Looker and Vertex AI as the stack transitions.

• Industry‑standard risk modelling concepts (stress testing, VaR/ES, PD/LGD).

• Model lifecycle & controls (documentation, validation, monitoring, and auditability).

• Stakeholder engagement across risk, finance, treasury, capital & tech.

• ~3 years with Python in a data or risk/quant adjacent role, including pandas/Num Py and writing testable, readable code.

• Solid SQL for data wrangling and reconciliation.

• Practical Git experience; familiarity with Git Lab 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.

• Experience in a bank/consultancy risk team or adjacent regulated environment.

• Exposure to Mongo
  
  DB, GCP, Looker, or Vertex AI.

• Familiarity with model monitoring/alerting and data quality frameworks.

We value ownership, pace, client focus, and raising the bar while staying collaborative and inclusive. Team members are encouraged to think big, automate where possible, and ship improvements continuously. Hybrid working with three days in the office supports collaboration and learning.