Manager, Data Science

You’re an established data science or advanced analytics leader with deep experience applying predictive models in a complex business environment.

You can operate confidently at both the technical and commercial levels. You’re able to challenge data scientists on model design and performance, then communicate the implications clearly to senior stakeholders. Most importantly, you have demonstrated that your work has influenced decisions, changed how a business operates or delivered measurable value.

This role requires:

Proven experience leading and developing a team of data science specialists.

Substantial hands-on experience in data science, predictive modelling or machine learning before moving into leadership.

Demonstrated experience developing, implementing and maintaining customer or member propensity models in a production environment.

Leading data discovery and partnering with business stakeholders to identify what is viable and meaningful within our data before we build. This may include data profiling, statistical analysis and rapid prototyping.

Evidence of translating model outputs into practical business decisions and measuring the resulting outcomes.

Strong technical knowledge of modelling methodology, feature development, model validation and performance monitoring.

Experience with model governance, including methodology documentation, drift monitoring, bias and fairness.

Advanced stakeholder engagement skills, including the ability to influence senior leaders and communicate complex findings clearly.

Experience evaluating analytical opportunities through a commercial, operational and risk lens.

Intermediate to advanced capability across Python and machine learning, with experience in Azure, MLOps or CI/CD environments.

Experience managing multiple analytical priorities within a complex and regulated organisation.

Financial crime and fraud literacy.

Experience within financial services, banking, insurance or superannuation will be highly regarded. An actuarial background may also be relevant where it is supported by strong contemporary data science capability, production modelling experience and demonstrated commercial application.