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Tier-1 bank, metro location, generalist Data Scientist title, and broad skills requirements drive high competition.
Credit risk modelling and Basel/IFRS9 familiarity create high domain specificity, limiting cross-industry transferability.
Explicit 1-3 years plus required modelling skills and regulatory familiarity yields medium strictness.
Lead development, maintenance, and enhancement of key credit risk models used for calculating credit risk RWA, provisioning, and obligor creditworthiness across CBA Group lending businesses.
Build statistical models and perform advanced analyses (e.g., Time Series, Macroeconomic Modeling, Non-linear Regression) using R and Python to generate insights for credit portfolios.
Collaborate with multiple internal teams including model validation, business units, project teams, and enterprise services to support credit modelling lifecycle and data preparation.
1-3 years relevant experience in data science or credit risk modelling.
Bachelor's, Master's, or PhD in Statistics, Mathematics, Data Science, Computer Science, or related field.
Proficiency in R or Python for statistical modelling; experience with Teradata SQL or Microsoft SQL for data management is advantageous.
Knowledge or familiarity with Basel regulatory standards, APRA regulations on credit risk, or IFRS9 collective provisions is advantageous.
Operates effectively in quantitative credit risk modelling within large financial services, particularly with exposure to credit risk regulatory frameworks.
Experienced in translating complex statistical analyses into actionable insights for stakeholders across risk and business functions.
Comfortable working collaboratively in multidisciplinary teams involving model development, validation, and implementation within a matrix organisational structure.