Director, MedTech Surgery Data Analytics & AI

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through 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 humanity. Learn more at jnj.com

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.

Job Function:

Technology Product & Platform Management

Job Sub Function:

Multi-Family Technology Product & Platform Management

Job Category:

Professional

All Job Posting Locations:

Raritan, New Jersey, United States of America

Job Description:

The Global Technology Leader – Director, MedTech Surgery Data & Analytics and AI will serve as the business-facing leader accountable for the data strategy, analytics outcomes, and AI enablement across MedTech Surgery—turning 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 controls—enabling 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 Surge