Contributing to the build of ECL models across wholesale and retail, which would be used to determine and manage loan loss provisions under the IFRS9 standard. This would include a diverse range of modelling projects by location, industry, division, complexity, and size, run according to ANZ standards and procedures.

Learning and running the current modelling processes to ensure efficient production of results.

Supporting her/his (Senior) Manager in investigating new modelling techniques and required data and investigating/researching the application of latest data science developments and machine learning techniques to ECL modelling.

Supporting model users in running the ECL models and interpreting the results, including any offline or out-of-cycle calculations as part of the ECL BAU process.

Presenting results in meetings and discussing results with the broader credit modelling team, i.e., retail modelling, wholesale modelling and stress test modelling.

Contributing to modelling team code libraries.

Providing updates to her/his (Senior) Manager.

Experience with analytical packages (R, Python, SQL, SAS).

1-2-year experience in a quantitative role, ideally within (retail or wholesale) credit risk modelling.

Strong analytical, numerical, research and problem-solving skills. Attention to details.

Excellent written and verbal communication.

Cloud computing and data skills.

Experience / exposure to banking data across retail / wholesale areas.

Knowledge of machine learning techniques.

Tertiary qualification in Finance, Statistics, Operations Research, Mathematics or Econometrics, or related field.