Description

Own the validation lifecycle for credit risk models, ensuring regulatory compliance and methodological rigor across new and existing implementations. You will assess data quality, model performance, and fit-for-purpose outcomes while driving continuous improvement in validation workflows.

This role is on-site.

Responsibilities

• Perform validation activities assessing data quality, modeling methodologies, and regulatory compliance (Basel, IRB, IFRS9, stress testing).
• Engage with model developers, owners, and monitors to communicate findings, risks, and recommendations effectively.
• Streamline validation workflows and uplift methodologies to enhance the quality, consistency, and efficiency of outcomes.
• Conduct end-to-end project delivery, managing time and stakeholder expectations for complex quantitative analyses.

Required Skills

• 3-6 years of experience in credit risk model validation or development.
• Proficiency in Python, R, SAS, and SQL for data analysis and statistical modeling.
• Deep understanding of Machine Learning, Credit Risk Modeling, and Model Validation principles.
• Knowledge of Basel Regulations, APRA IRB, and IFRS9 standards.
• Experience with Jupyter notebooks, R-markdown, and GitHub for version control and reporting.
• Ability to interpret and report complex quantitative material to diverse stakeholders.
• Bachelor's or Master's degree in Engineering (CS/IT) or quantitative disciplines (Mathematics, Statistics, Econometrics).

Preferred Skills

• Working knowledge of AI tools such as GitHub Copilot, GPT, or Claude.
• Experience with stress testing frameworks and post-implementation fit-for-purpose reviews.

Key Skills

Education

Any Graduate