Director, MedTech Surgery Data Analytics & AI

At Johnson & Johnsonwe believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented treated and curedwhere treatments are smarter and less invasive andsolutions are our expertise in Innovative Medicine and MedTech we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow and profoundly impact health for more at .

As guided by Our Credo Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson we respect the diversity and dignity of our employees and recognize their merit.

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Job Description:

The Global Technology Leader Director MedTech Surgery Data & Analytics and AIwill serve as the business-facing leader accountable for the data strategy analytics outcomes and AI enablement across MedTech Surgeryturning data into trusted compliant and scalable products that improve decision-making performance and innovation across R&D-adjacent commercial supply chain service and digital surgery domains.

This role will build and lead a team spanning data engineering analytics data product management and applied AI/ML/GenAI and will partner deeply with business and functional stakeholders to ensure business intimacy and measurable value realization.

Key Responsibilities

1) Strategy & Outcomes (Business Value)

• Co-create and execute a multi-year Data & AI strategy and roadmap for MedTech Surgery aligned to business priorities and transformation milestones; translate strategy into measurable outcomes and OKRs.
• Identify and prioritize high-impact use cases across Surgery domains (e.g. commercial growth demand sensing intelligent service computer vision for quality workflow automation) balancing near-term wins and scalable platforms.
• Establish value tracking (benefits adoption quality cycle-time) and regularly communicate progress to senior stakeholders.

2) Data as a Product (Trusted Standardized AI-Ready)

• Position data as a strategic reusable asset by creating and scaling data products for priority datasets with clear ownership lineage and quality controlsenabling trusted connected AI-ready insights
• Drive standardization for priority datasets (examples referenced in current OKR language include: UDI Product Regulatory Product Config Clinical) and enable secure access through approved marketplace patterns.
• Implement stewardship and operating cadences that strengthen data literacy and adoption across the Surgery organization.

3) Data Governance Risk and Compliance-by-Design

• Build and operationalize an enterprise-grade governance model for Surgery data and AI (policies controls decision forums stewardship) aligned to a federated model and consistent data management practices.
• Ensure privacy-by-design and security-by-design controls across data pipelines analytics products and AI solutions.
• Establish audit-ready processes for critical workflows (access controls traceability and monitoring) and partner with Cybersecurity Regulatory Affairs Legal and Quality functions.

4) AI Enablement & Model Lifecycle (From Pilot to Scale)

• Lead the end-to-end lifecycle for applied AI/ML/GenAI solutions: use case intake feasibility data readiness model development validation deployment monitoring and lifecycle governance.
• Enable scalable AI creation and deployment patterns aligned to Surgery platforms and labs including capabilities such as data ingestion enrichment/annotation cohorting/access model creation deployment and commercialization into clinical workflows where applicable.
• Champion responsible AI practices: transparency human oversight bias/risk assessment and ongoing performance monitoring (informed by common industry Responsible AI leader role patterns).

5) Platform & Architecture Partnership (Modern Data Stack)

• Define target-state data architecture for Surgery (integration patterns pipeline standards interoperability observability) and drive reusable components/patterns to accelerate delivery of data products.
• Partner with platform/architecture leaders to ensure scalable cloud foundations APIs and data platforms that can support analytics and AI workloads.
• Where relevant to digital surgery support pathways that interface with clinical environments (e.g. interoperability and integration patterns) as already referenced in existing digital-first strategy language.

6) Operating Model & Stakeholder Leadership (BU-Driven Enterprise-Connected)

• Establish a clear engagement model between BU-driven data science/engineering needs and central capabilities; ensure decision rights and resource allocation enable speed while maintaining standards.
• Serve as the primary point of accountability for Surgery Data & AI across Regions and Functions enabling cross-region leverage without losing business specificity.
• Represent Surgery in cross-enterprise councils/communities aligned to data standards governance and AI capability building.

7) People Leadership & Capability Building

• Build mentor and lead high-performing teams across data engineering analytics and applied AI; develop talent pipelines and role clarity (including upskilling and strategic hiring).
• Create a culture of product thinking operational excellence and continuous improvement; implement modern ways of working (agile/product delivery) with strong portfolio governance.

8) Financial & Vendor/Partner Management

• Own budget planning (where applicable) vendor strategy and partner ecosystem decisions for data/analytics tooling AI services and delivery capacity.
• Ensure cost discipline and scalable reusable delivery models.

Success Measures

• Increased adoption and satisfaction for priority data products; measurable improvements in data quality/availability for top datasets.
• AI use cases moved from pilot to scaled deployment with clear value realization and controlled lifecycle monitoring.
• Governance maturity improvements: clearer ownership lineage and audit-ready controls for critical workflows.
• Improved speed-to-insight and execution across Surgery value streams enabled by interoperable data platforms and trusted analytics.

Qualifications & Experience

• Masters or PhD in Data Science Computer Science Engineering or a related field; advanced business training (MBA or equivalent) is highly desirable.
• 10 years of experience in data & analytics leadership roles with a proven track record in AI strategy and implementation preferably within MedTech healthcare or life sciences.
• Demonstrated expertise in data governance cloud analytics platforms machine learning and regulatory compliance (GDPR HIPAA MDR).
• Strong leadership skills with the ability to inspire and manage multidisciplinary teams.
• Exceptional communication and stakeholder management abilities with experience collaborating across global organizations.

Core Competencies

• Strategic Vision & Execution
• Technical Leadership in AI & Analytics
• Change Management & Innovation
• Regulatory Acumen
• Collaboration & Influence

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