Credit Risk Modeling Scientist - Stress Testing & Insights

Commonwealth Bank; Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

Commonwealth Bank is seeking a Data Scientist - Credit Risk Models to develop and enhance stress-testing models for credit portfolios in a highly regulated environment. You will apply advanced statistics and data science to translate macroeconomic scenarios into measurable risk impacts and work with large, complex datasets.

You will collaborate with model users and validation teams, document assumptions, and explore AI-enabled approaches while preserving rigorous governance and human judgment.

Qualifications

• A tertiary qualification in a quantitative discipline such as Mathematics, Statistics, Econometrics, Engineering or Data Science with a strong academic performance in quants-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 techniques such as regression, time-series analysis, macroeconomic modelling or machine learning.
• Hands-on programming capability using R and/or Python, with experience using SQL to analyse complex datasets.
• The ability to work through ambiguous analytical problems, interrogate imperfect data and reach well-reasoned conclusions.
• Strong written and verbal communication skills, particularly the ability to explain methodology, assumptions, limitations and the practical implications of your analysis.
• Curiosity, sound judgement and a willingness to learn new modelling approaches and technologies.
• Experience with credit risk modelling, PD/LGD/EAD, IRB, IFRS 9 or stress testing would be highly regarded, though not an essential prerequisite.

Responsibilities

• Develop and enhance credit risk stress-testing models using advanced statistical and quantitative techniques.
• Apply approaches including 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 to understand how portfolios may respond under different conditions.
• Work with large and complex datasets, including incomplete or imperfect data, and determine practical, well-governed approaches to analytical challenges.
• Use R, Python and SQL, alongside relevant data and cloud platforms, to prepare data, perform analysis and develop models.
• Translate technically complex modelling outcomes into clear insights that can be understood by both technical and non-technical stakeholders.
• Document methodologies, assumptions, limitations and modelling decisions clearly, supporting independent review and validation.
• Partner across the modelling lifecycle with model users, independent model validation and business risk teams.
• Explore thoughtful uses of AI and emerging tools to improve analysis, learning, documentation and productivity, while maintaining appropriate human judgement and accountability.

Skills

Quantitative problem-solving

Statistical modelling

Data science

Regression

Time-series analysis

Macroeconomic modelling

Machine learning

Communication skills

Education

Quantitative degree (Math/Stats/Econometrics/Data Science)

Tools

R

Python

SQL

Job description

Commonwealth Bank is seeking a Data Scientist - Credit Risk Models to develop and enhance stress-testing models for credit portfolios in a highly regulated environment. You will apply advanced statistics and data science to translate macroeconomic scenarios into measurable risk impacts and work with large, complex datasets.

You will collaborate with model users and validation teams, document assumptions, and explore AI-enabled approaches while preserving rigorous governance and human judgment.