The Data Practice drives innovation and sets best practices for the effective and responsible use of data across the Whole-of-Government (WOG). We work at the intersection of data engineering, AI, privacy, and governance - developing standards, platforms, and tools that enable agencies to build high-quality data products and unlock the value of data for public good.
A core focus of our work is advancing responsible data use. As data sharing and AI adoption accelerate, we develop both the frameworks and technologies needed to ensure sensitive data can be used safely; including in AI systems, where interactions with sensitive data are becoming more widespread and risks will scale further with more autonomous, agentic workflows.
A core focus of our work is to make responsible data use practical and scalable. This includes developing technical guardrails, privacy-preserving workflows, evaluation methods, and reusable standards that help agencies use data safely for analytics, AI, and digital services. Our work spans areas such as AI privacy, context-aware anonymisation, synthetic data generation, federated AI / computing, encrypted computation, differential privacy, and confidential computing. We bridge the gap between policy, research, and real-world deployment by experimenting with new approaches, validating them through agency pilots, and operationalising them into standards, implementation guides, and central government tools.
We are seeking a Data Scientist at the intermediate to senior level. At the baseline, you will (a) conduct rigorous research and experimentation to evaluate emerging privacy solutions, (b) design scalable, implementable workflows tailored for government adoption, and © translate these into practical tools, guidelines, and systems used across agencies. Depending on experience and seniority, you may also take on broader ownership in shaping the team’s direction; identifying emerging technology opportunities, working with stakeholders across policy and engineering, and driving initiatives from problem definition through to scaled deployment.