Senior Credit Risk Modeller: Elevate PD/LGD & Stress

Harnham; Greater London; On-site; GBP 60,000 - 80,000; Full time

Job summary

Harnham is seeking an experienced Senior Credit Risk Controller to assist a UK lender in enhancing Credit Risk Rating and Stress Testing models. This hands-on role aims to improve methodologies across various lending portfolios while ensuring compliance with Basel 3.1 regulations.

The ideal candidate should have proven experience in Credit Risk modelling, strong knowledge of regulatory frameworks, and excellent technical skills in Excel, SQL, and programming languages like Python or R.

Qualifications

• Proven experience building or redeveloping Credit Risk Models or Scorecards.
• Strong knowledge of PD, LGD, EAD and Expected Loss modelling.
• Experience developing or enhancing ICAAP and Stress Testing models.
• Understanding of Basel, IRB and PRA regulatory frameworks.
• Experience within Asset Finance, Commercial Lending, Property Finance or specialist banking environments.
• Strong technical skills in Excel, SQL, Python, R or SAS.
• Excellent stakeholder management and communication skills.

Responsibilities

• Redevelop and enhance Credit Risk Rating (CRR), Scorecard and Credit Risk models.
• Redevelop ICAAP Stress Testing models for Pillar 2A and Pillar 2B assessments.
• Improve PD, LGD and Expected Loss methodologies across lending portfolios.
• Build product-specific risk frameworks reflecting key risk drivers.
• Perform model design, build, testing, implementation and documentation.
• Support model governance and regulatory requirements.
• Collaborate with Credit, Risk, Finance and Lending stakeholders.

Skills

Credit Risk Modelling

Stakeholder Management

Excel

SQL

Python

R

SAS

Job description

Harnham is seeking an experienced Senior Credit Risk Controller to assist a UK lender in enhancing Credit Risk Rating and Stress Testing models. This hands-on role aims to improve methodologies across various lending portfolios while ensuring compliance with Basel 3.1 regulations.

The ideal candidate should have proven experience in Credit Risk modelling, strong knowledge of regulatory frameworks, and excellent technical skills in Excel, SQL, and programming languages like Python or R.