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Tier-1 bank brand, metro location, and common Data Scientist title drive high applicant density.
Role requires banking credit-risk modelling and regulatory knowledge, limiting cross-industry transferability.
Explicit 1–3 years plus technical modelling and regulatory familiarity increases filter strictness moderately.
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Lead development, maintenance, and enhancement of key credit risk models used to calculate credit risk RWA, provisioning, and obligor creditworthiness.
Develop advanced statistical and econometric models (e.g., Time Series Analysis, Macroeconomic Modelling) using R and Python for credit portfolio insights.
Collaborate with stakeholders across the credit modelling lifecycle including validation, implementation, and risk teams to deliver impactful model outcomes.
1-3 years relevant work experience.
Tertiary qualification in a quantitative discipline such as mathematics, statistics, econometrics, actuarial science, finance, engineering, or data science.
Proficiency in R or Python for modelling; experience with Teradata SQL or Microsoft SQL is an advantage.
Work Experience Required: 1-3 years relevant experience.
Skilled in applying advanced mathematical and statistical techniques to credit risk modelling within a regulated financial environment.
Experience working on credit risk models aligned with Basel regulatory standards, APRA regulations, or IFRS9 collective provisions is advantageous.
Operates effectively in collaborative matrix teams interfacing across risk management, model validation, implementation, and business units.