Apply to VP - Quant Risk Model Validation

VP - Model Validation

The Role:

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.

Responsibilities:

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.

Qualifications:

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.

SLOANE | SHOREY

Sloane Shorey is a Ministry of Manpower Licensed Employment Agency: EA License 20S0307