Data Scientist - Credit Risk Models

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

Job summary

Commonwealth Bank of Australia in Sydney is seeking a Data Scientist - Credit Risk Models to develop and enhance stress-testing models for retail and non-retail portfolios. You will apply advanced statistical techniques to understand portfolio vulnerabilities and explain what drives model outcomes to both technical and non-technical stakeholders.

Join the Model Development Chapter within Risk Management to work with large datasets, document methodologies, and partner across validation and risk

Qualifications

• Tertiary qualification in a quantitative discipline.
• Experience in quantitative modelling and data science.
• Strong foundations in predictive modelling.
• Proficient in R and Python; SQL experience.
• Ability to solve ambiguous analytical problems.
• Excellent written and verbal communication.

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 modelling

Time-series analysis

Macroeconomic modelling

R or Python programming

SQL data analysis

Education

Bachelor's degree in Mathematics/Statistics/Econometrics/Data Science

Tools

R

Python

SQL

Job description

Build models that uncover how credit risk changes under stress

Apply advanced statistical modelling to complex portfolios

Turn quantitative analysis into meaningful risk insights

Do Work That Matters

Credit risk models play a critical role in helping CommBank understand how our lending portfolios could perform as economic conditions change. You'll work with complex data and challenging modelling problems, combining technical rigour with commercial and risk judgement to understand not simply what a model predicts, but why, what is driving the outcome and what it means for the portfolio.

See Yourself in Our Team

You'll join the Model Development Chapter Area within Risk Management, which develops credit risk models and advanced analytics across the Group.

We're recruiting across both Retail and Non-Retail Models, giving you the opportunity to apply your quantitative skills to complex credit portfolios and contribute to the development of stress-testing models and methodologies.

The broader Retail Models team develops estimates including Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD), with models supporting regulatory capital, provisioning and stress-testing activities.

Your Impact and Contribution

As a Data Scientist - Credit Risk Models, you'll apply advanced statistical and quantitative techniques to develop and enhance models that help us understand portfolio vulnerabilities, assess resilience under adverse scenarios and support better risk decisions. As such, you may regularly expect to:

• 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.

What You'll Bring

You'll be someone who genuinely enjoys quantitative problem-solving. You're comfortable getting into the detail of data and statistical methodology, but equally interested in understanding what the results mean and why they matter. To this role, you'll bring:

• A tertiary qualification in a quantitative discipline such as Mathematics, Statistics, Econometrics, Engineering or Data Science with a strong academic performance in your 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.

What's In It for You

This is an opportunity to deepen your technical capability in a sophisticated, highly regulated modelling environment where your work has a clear connection to real risk and business decisions. You'll gain exposure to forward-looking credit risk modelling, complex portfolio and economic scenarios, and experienced practitioners across modelling, validation, risk and the broader business. The role provides visibility beyond the mechanics of building models into how modelling insights inform portfolio management, planning and risk decision-making.

You’ll also be part of CommBank's broad