Job Description

• Perform independent validation of risk models to ensure accuracy, robustness, and fitness for purpose (margin, credit stress testing, derivatives pricing, collateral, liquidity stress, credit rating, VaR).
• Providing quantitative expertise, cross ‑functional support, and research input for new products and services.
• Provide risk evaluation and validation support for new product launches, including assessment of model design, assumptions, and risk controls.
• Ensure risk models are compliant with regulatory requirements.
• Drive digitalisation of model validation, maintaining and expanding automation capabilities through the effective use of analytics and AI.
• Explore and promote the responsible use of AI in risk management, including AI governance and AI safety.
• Contribute to thought leadership initiatives, including support for climate scenario analysis and modelling in collaboration with the sustainability team.

• Act as the primary point of contact for model validation activities, working closely with other colleagues in financial risk across analytics, model development and methodology, and risk control
• Scope and prioritise validation work.
• Deliver validation and analytics projects in partnership to support key initiatives.
• Produce quarterly validation reports, track findings, and ensure timely resolution of validation issues.
• Provide backup support to financial risk management, including default management contingencies.
• Demonstrate leadership potential, with the expectation of progressing to Team Lead in the future.

• Degree in data science, quantitative finance, engineering, mathematics or statistics.
• Postgraduate degree in data science or financial engineering preferred.
• 10-12+ years of progressive experience in risk analytics, model development, or model validation.
• Strong understanding of derivatives pricing models.
• Solid knowledge of market risk concepts, including risk factors, stress testing, VaR, mark ‑to ‑market, and risk sensitivities across asset classes.
• Good understanding of capital markets instruments, including fixed income, equities, FX, and commodities.
• Exposure to credit risk modelling is advantageous.
• Strong technical skills in Python, with experience implementing solutions in environments such as JupyterLab and using modern AI ‑assisted development tools (e.g. Claude Code, Gemini), version control (e.g. Bitbucket).
• Experience in developing, testing, implementing, and supporting analytics or risk solutions.
• Comfortable working with large datasets, data warehouses, and SQL.