Python Engineer – Financial Engineering (Credit Risk) | Global Banking A leading global bank is seeking a Python Engineer to join its Financial Engineering team within Global Risk Analytics team. This is a high-impact role focused on building and scaling core tooling used in credit risk model development across a global organisation.
You’ll sit within the Model Enablement team, working closely with Wholesale Credit Risk modellers to drive adoption of advanced toolkits and shape how risk models are developed, tested, and deployed at scale.
This is a highly collaborative role combining core engineering, stakeholder interaction, and influence on key risk modelling frameworks used across global portfolios.
Key Responsibilities
- Contribute to the design and development of a large-scale Python credit risk modelling toolkit.
- Work directly with risk modellers to ensure effective and efficient use of the toolkit.
- Gather user feedback and translate it into tooling improvements and new features.
- Build and maintain robust testing frameworks and ensure high code quality across the platform.
- Support the development of prototypes that influence risk analysis and measurement across the bank.
- Collaborate with global teams to ensure consistency and scalability of model development practices.
Skills & Experience
- Strong hands-on Python development experience in a professional environment.
- Solid understanding of object-oriented programming, SOLID principles, and design patterns.
- Experience with test-driven development and testing frameworks (e.g. pytest).
- Strong awareness of clean code principles and code review best practices.
- Experience working with version control (Git) and collaborative development workflows.
- Exposure to Agile environments and CI/CD practices.
- Strong communication skills and ability to work closely with technical end users.
Nice to Have
- MSc or PhD in a technical discipline (Computer Science, Mathematics, Physics, etc.).
- Knowledge of credit risk modelling or financial risk analytics.
- Experience with the Python scientific stack (NumPy, pandas, SciPy, etc.).
- Experience with PySpark or big data tooling.
- Exposure to building or contributing to Python libraries or open-source projects.
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