Role Summary

Develop predictive machine learning models that evaluate retail and commercial credit risk, detect financial stress early, and automate home lending decisions.

Key Responsibilities

• Train interpretable machine learning models (XGBoost, LightGBM, neural networks) on massive transactional datasets.
• Ensure model adherence to APRA Prudential Standards (APS 112/113) and fair lending guidelines.

Required Qualifications

• Master’s or PhD in Statistics, Data Science, or Econometrics with 5+ years banking or credit risk data science experience.

Remuneration

• $165,000 – $205,000 AUD base + bonus + 11.5% Superannuation.