Senior Data Scientist - Stress Testing Models

Commonwealth Bank of Australia; Sydney; On-site; AUD 140,000 - 180,000; Full time

Job summary

Commonwealth Bank of Australia is seeking a Senior Data Scientist - Stress Testing Models to lead the development of credit risk stress-testing models across the Group. You will translate macroeconomic scenarios into PD, LGD and EAD impacts, providing insights to support enterprise stress testing and risk decisions.

You will work with R, Python, SQL, Teradata and AWS to prepare and analyse data, validate models, and communicate methodologies to diverse stakeholders.

Qualifications

• Strong experience in quantitative statistical modelling and data science for credit risk.
• Knowledge of PD, LGD, EAD and macroeconomic modelling is essential.
• Proficiency in R, Python and SQL with large data handling experience.

Responsibilities

• Lead development of credit risk stress-testing models across the Group.
• Translate macroeconomic scenarios into credit risk impacts (PD/LGD/EAD).
• Analyse model behaviour, limitations and key risk drivers for risk decisions.
• Collaborate with stakeholders across model validation, risk, Finance and IT.

Skills

Quantitative modelling

Data science

Econometrics

Credit risk analytics

R

Python

SQL

Teradata

AWS

IFRS 9

IRB

Macroeconomic modelling

Education

Tertiary qualification in Mathematics/Statistics/Econometrics/Data Science

Tools

R

Python

SQL

Teradata

AWS

Job description

Model how credit portfolios respond under stress

Apply advanced statistics to real-world risk challenges

Turn complex modelling into meaningful risk insights

See Yourself in Our Team

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

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

This is an opportunity to combine deep quantitative thinking with real-world application, using data, statistical techniques and emerging AI tools to solve complex modelling problems and help inform important risk and business decisions. 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