Data Scientist Senior Associate

Impact & contribution

Roles & Responsibilities

• Generating insights for CBA's credit portfolios, by building statistical models and performing analyses with sound application of advanced statistical and econometric techniques including, but not limited to, Time Series Analysis, Macroeconomic Modelling and Non-linear Regression, using R and Python;
• Actively contributing to projects to develop and enhance credit risk models, delivering outcomes with minimal supervision;
• Analysing the business impact of the models including changes to the Expected Loss, capital and Risk Weighted Assets as a result of model improvements or refreshes;
• Thoroughly documenting the modelling results and the thought process around the choice of modelling methodology in a concise and user-friendly form, to enable stakeholders to independently validate the final model and assess its impact;
• Working with the Group’s data resources, as well as key data-related projects, to obtain and prepare data for modelling purposes as required. This will involve working with SAS, Teradata SQL, or other related tools; and
• Interacting with stakeholders within business units and group model validation teams to seek input and share results on model development efforts as needed.

Essential Skills

• 1-3 years relevant experience
• Demonstrated skills in written and verbal communication, including ability to interpret and report complex material and make sound recommendations to a range of stakeholders;
• Diligent, collaborative and entrepreneurial workstyle delivering error-free output using innovative techniques and working with remote and on-site teams across the unit to drive improved risk analytics;
• Ability to work productively as part of a team on-site, remotely and in isolation as required;
• Experience in modelling using R or Python, and working and maintaining datasets using Teradata SQL or Microsoft SQL is an advantage;
• Sound time management and prioritization;
• Apply knowledge of business and regulatory drivers when analysing model outcomes;
• Familiarity or experience with the Basel regulatory standards and relevant APRA regulations regarding credit risk, or IFRS9 requirements for collective provisions is an advantage .

Education Qualification

• A solid foundation in applying advanced mathematical and statistical techniques and familiarity/experience with relevant tools such as R and Python, conducting modelling, data manipulation and data visualization;
• Tertiary qualifications in a quantitative discipline such as mathematics, statistics, econometrics, actuarial science, finance, engineering, data science or any other related discipline.
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