VP, Data & Digital Quality

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:

Quality

Job Sub Function:

Digital Quality

Job Category:

People Leader

All Job Posting Locations:

Horsham, Pennsylvania, United States of America, New Brunswick, New Jersey, United States of America

Job Description:

The VP, Data & Digital Quality is accountable for architecting and executing the digital and data strategy that will reimagine Quality through advanced analytics, AI/ML, agentic orchestration, and next‑generation risk sensing capabilities. This leader accelerates the transformation from reactive compliance to predictive, intelligence‑driven Quality, leveraging enterprise data, automation, and digital products to improve risk visibility, strengthen decision‑making, and enhance patient safety.

The role shapes the long‑term digital quality vision, drives the global roadmap, and ensures high‑quality delivery of platforms, predictive models, emerging technologies, and compliance systems across all regions and segments. and supports the Q&C digital talent development strategy.

This leader partners with Regulatory, Technology, Business leaders and Quality colleagues to drive measurable improvements in quality outcomes and audit readiness by enabling the evolution of Quality practices to incorporate emerging technologies.

Major Duties & Responsibilities:

Digital Product Strategy & Portfolio Delivery

• Own and continuously evolve the enterprise portion of the Q&C Digital Strategy & Data 5‑Year Roadmap, including integration into long-range financial planning,  business planning and refresh cycles. Oversee and set high-level strategy for sector Digital Strategy & Roadmap.

• Lead the strategic shift toward predictive digital quality, ensuring digital product roadmaps incorporate AI?ML, advanced analytics, data quality and adaptive risk models that proactively identify issues before they impact patients or compliance.

• Drive the integration of advanced technologies including automated and agentic risk alerts into Quality operations.

Data Platforms, Architecture & Visualization

• Build and scale an enterprise quality data architecture (structured and unstructured) that enables near‑real‑time data ingestion (where applicable), harmonization,  AI-ready data quality, and modeling to support rapid detection of trends, improves predictive and preventive capabilities, and accelerates Quality decision‑making.

• Advance capabilities in risk dashboards, anomaly detection, and quality signal intelligence using tools such as QUALIFI, ThoughtSpot, and modern cloud analytics stacks.

Drive Adoption of Emerging Technologies

Either at an enterprise level, or through support at sector level:

• Build and operationalize an AI/ML strategy for Quality, including use cases in process monitoring, automated document intelligence, complaint/risk signal extraction, audit prediction, and failure mode analytics.

• Scale GenAI and Agentic AI enabled quality workflows (e.g., intelligent QMS search, automated CAPA summarization, QA review assistance) while maintaining regulatory rigor and responsible AI guardrails.

• Oversee development of early‑warning systems that detect, contextualize, and escalate quality risks using multivariate models and cross‑value‑chain data signals.

• Champion responsible AI practices, ensuring models meet requirements for validation, transparency, explainability, and audit readiness.

• Evolve practices across Enterprise Quality to incorporate AI/ ML model validation, continuous monitoring, and audit‑ready AI governance frameworks.

• Partners with Regulatory, Technology, an