Senior Liquidity and Market Model Risk Architect

Barclay Simpson; Greater London; Hybrid; GBP 75,000 - 110,000; Full time

Job summary

Barclay Simpson is seeking a technically adept risk professional to join its Liquidity and Market Risk team in London with hybrid working (2 days in the office). The role focuses on stress testing, model ownership, validation-style testing and deeper analysis of model algorithms, code and data.

You will monitor and govern risk models, test assumptions, and develop benchmarking models. A strong technical and quantitative background in model risk, validation or governance is essential.

Qualifications

• Experience in model validation, model risk, or model governance.
• Strong SQL, Python and R skills with ability to analyse complex models.
• Ability to interrogate algorithms and data, and work with large datasets.
• Experience communicating technical conclusions to technical and non-technical audiences.

Responsibilities

• Perform daily, weekly, monthly and quarterly model monitoring and governance activities.
• Test model assumptions, parameters, algorithms and outputs.
• Stress test models using extreme scenarios to identify weaknesses.
• Develop benchmarking and challenger models to assess model performance.
• Maintain model documentation in line with model risk policy.
• Run liquidity stress tests and assess impact of new products and currencies.
• Extract, clean and analyse large datasets; improve code and automate processes.

Skills

Advanced SQL

Python

R

Model risk

Model validation

Model governance

Quantitative analysis

Large datasets

Code improvement

Stakeholder communication

Job description

Barclay Simpson is seeking a technically adept risk professional to join its Liquidity and Market Risk team in London with hybrid working (2 days in the office). The role focuses on stress testing, model ownership, validation-style testing and deeper analysis of model algorithms, code and data.

You will monitor and govern risk models, test assumptions, and develop benchmarking models. A strong technical and quantitative background in model risk, validation or governance is essential.