Data Scientist

Perform end-to-end validation of credit risk models (IRB, IFRS9, Stress Testing) ensuring data quality, regulatory compliance, and model performance across the model lifecycle.

Engage proactively with stakeholders including model developers and owners to communicate validation findings, risks, and recommendations.

Contribute to strategic transformation and continuous improvement in validation methodologies, workflows, and tools to enhance quality and efficiency.

5-8 years experience in credit risk model validation or development specifically with IRB, IFRS9, and stress testing models.

Bachelor's or Master's degree in engineering, computer science, information technology, mathematics, statistics, or econometrics with above average academic achievements.

Mandatory programming skills in SAS, R, Python, and SQL; working knowledge of Jupyter notebooks or R-markdown and GitHub is advantageous.

Experience with Basel regulatory standards on credit risk is essential; experience with APRA regulations on IRB, IFRS9, and stress testing is advantageous.

Experienced quantitative analyst comfortable working within a model risk and validation framework for credit risk models.

Skilled in communicating complex validation findings clearly to diverse stakeholders including technical and regulatory audiences.

Capable of contributing to and driving continuous process and methodological improvements in a regulated financial environment.