Through the typical life-cycle of quantitative model development work, you will collaborate with various Risk teams and key business units. This starts from working with the Risk Policy team to design a conceptual model that meets the key policy and business objectives.
Thereafter, development work on the model will progress to expression in Python code for rigorous analysis (sensitivity, what-if, model validation) and user testing, in order to meet governance and approval requirements.
Prime examples of quantitative models used to enable critical risk management functions includes: margin model, stress testing liquidity models, risk appetite, pricing models etc.
- Develop quantitative risk models, including margin, stress testing, credit risk, and liquidity risk models.
- Use advanced statistical techniques to assess exposures and identify evolving and new risks;
- Perform sensitivity and scenario analyses for model resilience.
- Create risk assessment tools and models in Python.
- Utilise Git and Jira for efficient code management.
- Sound technical understanding of code scalability and stability, to aid deployment of model into productionised tech environment.