Data Scientist - Credit Risk Models

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

Benefits offered by this job

Data Science community

Accessible workplace & adjustments

Supportive risk and validation teams

Job summary

CommBank in Sydney seeks a Data Scientist - Credit Risk Models to develop and enhance credit risk models and stress-testing methodologies. You will tackle complex data, apply advanced statistical and quantitative techniques, and translate portfolio scenarios into measurable credit-risk impacts for decision makers.

You will work within the Model Development Chapter Area of Risk Management across Retail and Non-Retail Models, collaborating with validation and risk teams, using R, Python and SQL,

Qualifications

• A tertiary qualification in Mathematics, Statistics, Econometrics, Engineering or Data Science with strong quant modules.
• Experience in quantitative modelling, data science or credit risk analytics.
• Strong foundations in statistical and predictive modelling and machine learning.
• Hands-on programming with R and/or Python; SQL for data analysis.
• Able to explain methodology, assumptions and implications clearly.
• Willingness to learn new modelling approaches and technologies.
• Experience with PD/LGD/EAD, IRB, IFRS 9 or stress testing is highly regarded.

Responsibilities

• Develop and enhance credit risk stress-testing models using advanced techniques.
• Apply predictive modelling, regression, time-series and macroeconomic methods.
• Translate scenarios into measurable credit-risk impacts (PD, LGD, EAD, ECL).
• Analyse model behaviour, sensitivities, limitations and key drivers.
• Work with large, complex datasets and govern analytical approaches.
• Use R, Python and SQL; leverage cloud platforms for data prep and modeling.
• Translate complex results into clear insights for technical and non-technical stakeholders.
• Document methodologies, assumptions, limitations and modelling decisions.

Skills

Quantitative problem-solving

Statistical modelling

Data science

R and Python

SQL proficiency

Ambiguity handling

Communication skills

Credit risk modelling

Education

Quantitative degree

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 broader Data Science community, which connects Data Scientists across the Group through shared capability frameworks, learning pathways, tooling and AI-enabled ways of working.

Working With Us

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