Data Scientist - Credit Risk Models

About the role

CommBank is recruiting a Data Scientist for its Credit Risk Models team within the Model Development chapter of Risk Management. The role applies advanced statistical and quantitative techniques to build and enhance models that show how lending portfolios behave under changing economic conditions, including stress-testing models and methodologies. It covers both Retail and Non-Retail models, with the Retail Models team developing estimates such as Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD).

Key responsibilities

• Develop and enhance credit risk stress-testing models using predictive modelling, regression, time-series analysis, panel-data methods and macroeconomic modelling.
• Translate economic and portfolio scenarios into measurable credit-risk impacts, including changes in PD, LGD, exposure and expected credit loss.
• Analyse model behaviour, sensitivities, limitations and key risk drivers.
• Work with large, complex datasets, including incomplete or imperfect data, and use R, Python and SQL with data and cloud platforms to prepare data, run analysis and build models.
• Turn complex modelling outcomes into clear insights for technical and non-technical stakeholders.
• Document methodologies, assumptions, limitations and modelling decisions, and partner with model users, independent model validation and business risk teams.
• Explore uses of AI and emerging tools to improve analysis, learning, documentation and productivity.

Required profile

• Tertiary qualification in a quantitative discipline such as Mathematics, Statistics, Econometrics, Engineering or Data Science, with strong academic performance in quant-heavy modules.
• Experience in quantitative modelling, statistical modelling, data science, econometrics, actuarial science, credit risk analytics or another highly quantitative discipline.
• Strong foundations in statistical and predictive modelling, with exposure to regression, time-series analysis, macroeconomic modelling or machine learning.
• Ability to work through ambiguous problems, interrogate imperfect data and reach well-reasoned conclusions.
• Strong written and verbal communication skills, and curiosity and sound judgement.
• Experience with credit risk modelling, PD/LGD/EAD, IRB, IFRS 9 or stress testing is highly regarded but not essential.

Required skills

• Hands-on programming in R and/or Python, with SQL for analysing complex datasets.
• Statistical and predictive modelling, including regression, time-series analysis and macroeconomic modelling.

The role sits in a highly regulated modelling environment with access to experienced practitioners across modelling, validation, risk and the business, and is part of CommBank's Data Science community. Applicants who are already part of the Commonwealth Bank Group must apply through the group's internal application process.