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Tier-1 bank, metro location, popular Data Scientist title, and mid-level experience increase applicant competition.
Requires credit-risk, Basel/IFRS9 and bank-specific modelling, limiting transferability across industries.
Explicit 1–3 years requirement plus expected R/Python and credit-modelling skills make filters moderately strict.
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Lead development, maintenance, and enhancement of key credit risk models used for calculating credit risk RWA and provisioning across all CBA lending business units.
Build statistical models and perform advanced data analyses (e.g., Time Series, Macroeconomic Modelling) using R and Python to generate insights for credit portfolios.
Collaborate with cross-functional teams (model validation, data projects, enterprise services, business unit risk teams) to deliver model outcomes and support the credit modelling lifecycle.
1-3 years relevant experience in data science or credit risk modelling.
Tertiary degree in a quantitative discipline such as mathematics, statistics, econometrics, actuarial science, finance, engineering, or data science.
Proficiency in R or Python for modelling and experience with SQL tools like Teradata SQL or Microsoft SQL.
Work Experience Required: 1-3 years; Notice Period: Not explicitly mentioned in the JD.
Experience working with credit risk modelling frameworks inclusive of Basel standards, APRA regulations, or IFRS9 collective provision requirements is advantageous.
Able to operate independently with minimal supervision while collaborating within distributed teams including remote and on-site members.
Strong ability to communicate complex statistical concepts and model impacts clearly to varied stakeholders, including business and validation teams.