Head of Data Science

**Head of Data Science & AI**

**Summary**:
Define & execute a roadmap for adoption & implementation of AI that aligns with broader Data & AI strategy.
- 10 yrs of experience in data science, AI, machine learning with proven leadership.
- 5+ yrs in a leadership role in AI risk management & implementation of responsible AI practices
- Permanent Role| Sydney office| Hybrid working environment

**About the Role**

The Head of Data Science & AI is a key role with a visionary focus and technical proficiency, responsible for driving icare’s AI and data science led business transformation strategy. The role requires deep technical understanding of machine learning (ML), Artificial intelligence coupled with ability to influence senior leadership and build and operate scaled enterprise level AI operations. The role will demonstrate both thought leadership and technically expertise, leading the team in designing and deploying impactful AI solutions that drive innovation and operational excellence across icare.

**Benefits**
- A corporate wellbeing program with subsidised gym membership, free flu vaccinations and health check programs.
- icare day - access to an extra day’s leave.
- Comprehensive learning and development support aligned to icare’s Core Capabilities.
- Our People Awards - On-the-spot Recognition, Quarterly Values Awards & Our People Annual Awards.
- Access to our Employee Assistance Program.

**Duties**

It’s an exciting time at icare where no two days are the same: as **Head of Data Science& AI **you will:

- Define and execute a roadmap for adoption and implementation of AI (including GenAI) that aligns with broader Data and AI strategy, ensuring the integration of AI technologies into business processes to digitally uplift and enhance icare’s service delivery.
- Influence and engage senior leadership and stakeholders by articulating the strategic value of data science and AI, driving buy-ins and essential AI infrastructure.
- Collaboratively work with business leaders to identify opportunities and use cases to embed AI into business processes to transform and uplift service operations.
- Lead the development and implementation of risk assessment, model interpretability, bias detection and fairness frameworks, ensuring AI solutions adheres to regulatory and ethical standards.
- Oversee the establishment of controls to mitigate model risks, including data privacy risk, operational risks, bias-related risks while the algorithm remains auditable and trustworthy
- Working with Governance and Management team to ensure the processes are aligned to overall Data and AI governance frameworks.
- Act as a primary contact for regulatory compliance in AI and Data science ensuring models meet the latest legal requirements, aligned to NSW AI framework and other emerging voluntary standards and guidelines.
- Lead the architecture and oversee the development of end-to-end AI/ML lifecycle, including data collections, model development, deployment, monitoring and AI agentic flow to ensure icare can use AI in a scalable and reliable way.
- Oversee the setup of MLOps and ModelOps capability ensuring alignment to risk policies and procedures.
- Serve as the technical escalation point, guiding complex model design decisions, troubleshooting model issues, ensuring technical soundness in ML algorithms and infrastructure
- Partner with architects to design AI-friendly infrastructure that supports high availability, low latency and data governance, optimising for both batch and real-time AI use cases.
- Champion a forward-looking AI agenda by driving advanced R&D initiatives, including deep learning, reinforcement learning and other novel AI approach in a risk managed environment
- Oversee and guide implementation and optimisation of MLOps frameworks and pipelines, including CICD for ML models, automated testing to support rapid model iteration and deployment.
- Oversee the design, development, and deployment of AI/ML models, including GenAI, computer vision, recommendation systems, and predictive analytics.
- Foster a culture of experimentation by identifying and prioritising use cases that deliver business impact.
- Establish partnership with external academic, industry and government institutions to stay at the forefront of responsible AI advancement and contribute to though leadership in AI.
- Ensure the infrastructure and tools are scalable, flexible, and capable of supporting both current and future AI initiatives.
- Implement automated machine learning (AutoML) tools and platforms to streamline the development and deployment of AI models.
- Actively build team capability through driving proactive training, hiring and team skill management
- Foster a culture of innovation and continuous improvement within the Data science and AI team.
- Stay up to date with industry trends and emerging technologies, ensuring the team is equipped with cutting-edge tools and practices by forecasting future needs for technology, i