Senior Data Governance & AI Quality Lead

Philips International; Plymouth (MA); On-site; USD 129,000 - 205,000; Full time

Job summary

Philips seeks a Sr. R&D Data Governance & QMS Lead to establish data readiness, governance, and quality processes supporting scalable AI innovation across programs worldwide. Responsibilities include governing software, AI, hardware data with privacy, security and regulatory compliance.

You will work with Legal, Privacy, Quality and Procurement to define data rights, residency, and controls, while ensuring de-identification, metadata, lineage, and audit readiness across datasets and partnerships.

Qualifications

• 5+ years of experience leading data governance and QMS processes in regulated environments (medical devices/healthcare).
• Experience embedding governance requirements into data intake, processing, de-identification, and release controls.

Responsibilities

• Establish and improve the data governance and QMS framework for software, AI, hardware-generated data.
• Lead governance and QMS workstreams across hospitals, research-sites, vendors, and data arrangements.
• Define data rights, residency, cross-border constraints, controls, approvals, and obligations with Legal, Privacy, Quality, Security and Procurement.
• Coordinate evidence, remediation, and audit readiness for audits and inspections.

Skills

HIPAA & GDPR knowledge

Data governance leadership

AI/ML data governance

Data residency & cross-border

Regulatory compliance

Education

Bachelor's degree

9+ years of related experience in lieu of a degree

Job description

Philips seeks a Sr. R&D Data Governance & QMS Lead to establish data readiness, governance, and quality processes supporting scalable AI innovation across programs worldwide. Responsibilities include governing software, AI, hardware data with privacy, security and regulatory compliance.

You will work with Legal, Privacy, Quality and Procurement to define data rights, residency, and controls, while ensuring de-identification, metadata, lineage, and audit readiness across datasets and partnerships.