Senior Data Scientist in Sydney

Apply advanced statistics to real-world risk challenges and turn complex modelling into meaningful risk insights.

The Model Development Chapter Area sits within Risk Management and is responsible for developing credit risk models and advanced analytics across the Group. You'll join a collaborative modelling environment, working with colleagues across model development, independent model validation, business unit risk, Finance and Enterprise Services. Your work will span retail and non-retail credit exposures, giving you the opportunity to tackle complex modelling challenges across different portfolio types and understand how changing economic conditions can affect their risk profiles.

Your Impact and Contribution

As a Senior Data Scientist - Stress Testing Models, you'll use advanced quantitative modelling and analytics to help answer questions about how changing economic conditions affect the credit quality of a portfolio, its vulnerabilities, and how losses could evolve under different scenarios. Your work will support enterprise stress testing and planning, while providing meaningful insight into the drivers of portfolio risk, resilience and potential credit losses.

In this role, you will:

• Lead the development and enhancement of credit risk stress-testing models used across the Group.
• Build statistical models and conduct advanced analysis using techniques such as predictive modelling, non-linear regression, time-series analysis, panel-data methods and macroeconomic modelling.
• Translate macroeconomic and portfolio scenarios into credit risk impacts, including changes in Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD) and expected credit loss.
• Analyse model behaviour, sensitivities, limitations and key risk drivers to understand what stress-testing results mean for portfolio resilience and risk decisions.
• Work with large, complex and sometimes imperfect datasets, applying sound judgement to identify practical and well-governed approaches to data challenges.
• Use R, Python and SQL, alongside tools and platforms including Teradata and AWS, to prepare, transform and analyse data for modelling.
• Clearly articulate modelling methodologies, assumptions, limitations and conclusions so your work can be understood, challenged and independently validated.
• Partner with model users, independent model validation, business unit risk teams, Finance and Enterprise Services throughout the modelling lifecycle.
• Provide technical guidance and constructive review to other analysts, contributing to strong modelling standards and reusable analytical practices.
• Explore responsible applications of AI to enhance analysis, model development, documentation, learning and productivity, while maintaining accountability for final outcomes.

What You'll Bring

You're a naturally curious quantitative thinker who enjoys getting beneath the numbers. You can take an ambiguous modelling problem, work through complex or imperfect data, test different approaches and reach a well-reasoned conclusion. Just as importantly, you can explain what your analysis means and why it matters, whether you're speaking with another modeller or a non-technical stakeholder.

You'll bring a combination of:

• Strong experience in quantitative statistical modelling, data science, econometrics or credit risk analytics, with the ability to independently solve complex modelling and data problems.
• Strong knowledge of statistical, econometric or predictive modelling techniques, which may include logistic and non-linear regression, time-series analysis, panel modelling, macroeconomic modelling and machine learning.
• Hands-on capability in R and/or Python, together with SQL and experience working with large and complex datasets.
• Experience in credit risk modelling, including PD, LGD or EAD models, stress testing, IFRS 9 or IRB, will be highly regarded.
• The ability to structure ambiguous problems, challenge assumptions and exercise sound judgement when the data or answer isn’t straightforward.
• Strong written and verbal communication skills, including the ability to explain methodology, conclusions, caveats and the "so what" behind your analysis.
• Experience leading pieces of analytical work, influencing stakeholders and providing guidance or constructive review to less experienced colleagues.
• Curiosity about how AI can be used responsibly to improve modelling, analysis, documentation and ways of working.
• Tertiary qualifications in a quantitative discipline such as Mathematics, Statistics, Econometrics, Engineering or Data Science.

What's In It for You

This is an opportunity to deepen your technical capability in an area of data science where modelling has a clear connection to significant business, risk and regulatory decisions. You'll work on forward-looking stress-testing models, exploring how economic conditions and portfolio characteristics interact to influence credit risk. You'll also gain exposure across the modelling lifecycle, working with credit risk specialists, Finance, model validation and business stakeholders to see how quantitative analysis is applied in strategic planning, portfolio management and risk decision-making.

You'll be encouraged to keep developing your craft, explore emerging analytical and AI capabilities, share your expertise with others and contribute to how modelling practices evolve across the team.

At CommBank, we're committed to creating an accessible, inclusive and respectful workplace. If you require support or adjustments, please let us know. We welcome applications from people of all backgrounds and we're particularly committed to making a positive difference for Aboriginal and/or Torres Strait Islander Peoples.