The main responsibilities of the position include (but are not limited to):
Innovation Pathways & Planning
* Champion structured innovation management aligned with responsible AI development principles, shaping delivery approaches, validating problem statements, and assessing technical feasibility.
* Act as a key link between AI intake, assessment, and approval processes and delivery-focused innovation teams.
Delivery Planning & Execution (Agile / Hybrid)
* Develop and maintain integrated delivery plans, iteration cadences, and cross-team dependencies spanning data, engineering, clinical, and operational functions.
* Lead delivery ceremonies (including planning, stand-ups, and reviews), track scope and forecast versus actuals, and drive corrective actions to protect outcomes and timelines.
Governance, Risk & Compliance
* Ensure adherence to defined AI lifecycle standards, including architecture review, validation, deployment, ongoing monitoring, incident management, and model change control.
* Coordinate privacy, safety, and information security assessments, maintaining appropriate evidence to support ethical AI guardrails and compliance obligations.
Stakeholder Management & Communication
* Engage stakeholders across multiple business units, maintaining transparent communication around purpose, benefits, impacts, and trade-offs.
* Translate technical detail into clear business outcomes and decision options, preparing concise updates for steering forums and senior leadership.
Value Realisation & Adoption
* Define measurable value indicators at mobilisation and track benefit realisation (financial, operational, and service outcomes) post-launch.
* Coordinate training, readiness, and transition activities to support adoption, operational handover, post-deployment monitoring, and sustained value delivery.
Key Skills:
* Proven project management (≥5-7 years) delivering data/analytics/AI or digital initiatives in complex, multi‑brand environments.
* Strong Agile/hybrid delivery; mastery of planning, RAID, dependency management, and benefits tracking.
* Working knowledge of AI/ML delivery stages (data prep, model dev/validation, deployment, monitoring) and associated governance controls.
* Familiarity with privacy, information‑security and responsible AI considerations in healthcare/adjacent domains.
* Tools: ADO/Jira, Confluence, MS Project, Power BI (or equivalents).
* Outcome‑focused, structured, and resilient; excellent communication and stakeholder influence.
* Risk‑aware and ethical; balances delivery velocity with safety/compliance.
* Collaborative; low‑ego leadership across clinical, operational, data and engineering teams.
Reference Number: PJ
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