Data Scientist – Credit Risk Models

Commonwealth Bank

. Develop and enhance credit risk stress-testing models using advanced statistical and quantitative techniques .

Posted 10/27/2026full-timeSydney • AustraliaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and enhancing credit risk stress-testing models using advanced statistical techniques, predictive modelling, and data analysis. Proficient in translating complex modelling outcomes into actionable insights for diverse stakeholders while maintaining a strong foundation in quantitative disciplines.

Highest-signal resume keywords

Quantitative ModellingStatistical ModellingR ProgrammingPython ProgrammingSQL Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModellingRegression AnalysisTime-Series AnalysisMacroeconomic ModellingData ScienceCredit Risk AnalyticsMachine LearningModel ValidationStress TestingData Analysis

Soft Skills

Strong Communication SkillsCuriositySound JudgementProblem-SolvingWillingness to Learn

Tools & Technologies

RPythonSQLData PlatformsCloud Platforms

Industry Keywords

Credit Risk ModellingPDLGDEADIRBIFRS 9Stress TestingQuantitative DisciplineEconometricsActuarial Science

Tech Stack

Tools & technologies

CloudPythonSQL

About the role

Key responsibilities & impact

Develop and enhance credit risk stress-testing models using advanced statistical and quantitative techniques
Apply 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
Work with large and complex datasets, including incomplete or imperfect data
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 for technical and non-technical stakeholders
Document methodologies, assumptions, limitations and modelling decisions
Partner with model users, independent model validation and business risk teams across the modelling lifecycle
Explore thoughtful uses of AI and emerging tools while maintaining human judgement and accountability

Requirements

What you’ll need

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

Benefits

Comp & perks

Opportunity to deepen technical capability in a sophisticated, highly regulated modelling environment
Exposure to forward-looking credit risk modelling, complex portfolio and economic scenarios, and experienced practitioners
Visibility into how modelling insights inform portfolio management, planning and risk decision-making
Participation in CommBank’s broader Data Science community
Shared capability frameworks and learning pathways
Tooling and AI-enabled ways of working
Accessible, inclusive and respectful workplace
Workplace support or adjustments available
Learning and professional development through the Data Science community