Philips

Overview

Owns the product vision and roadmap for assigned AI use cases within MR Service Innovation, aligning each to programme strategy and measurable business outcomes.

About Philips

We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.

Requirements & Eligibility

• A Master’s degree or Bachelor's Degree in Business, Engineering, Computer Science, Data Analytics, or equivalent / MBA preferred.
• Minimum 10 years of relevant experience, including demonstrable experience in product management and/or business analysis for data, analytics, or AI/ML products.
• Experience working in agile delivery teams and translating business problems into technical requirements is required.
• Experience in a regulated industry (healthcare, medical devices, automotive, or similar) and exposure to AI/ML product delivery is strongly preferred.
• Demonstrated research mindset, such as publications, workflow improvements, or new modelling techniques.

Key Responsibilities

• Owns the product vision and roadmap for assigned AI use cases within MR Service Innovation, aligning each to programme strategy and measurable business outcomes.
• Discovers, frames, and validates AI opportunities by analysing service, contract, and case-level data to confirm the problem is real, sized, and worth solving before build begins.
• Builds and maintains the business case for each initiative — quantifying impact, cost, ROI, and risk — and supports prioritisation decisions across the AI portfolio.
• Elicits, documents, and prioritises requirements; writes clear epics, user stories, and acceptance criteria that translate business needs into actionable work for data science and engineering teams.
• Owns and continuously refines the product backlog, balancing business value, delivery complexity, data readiness, and regulatory considerations.
• Defines success metrics and KPIs for each AI product, and tracks value realisation and adoption post-deployment, feeding learnings back into the roadmap.
• Partners closely with data scientists, ML/data engineers, and external delivery partners to ensure solutions are feasible, well-scoped, and delivered to agreed outcomes.
• Acts as the primary liaison between business stakeholders and the delivery team, managing expectations, sequencing decisions, and keeping stakeholders aligned through delivery.