Principal, R&D Digital Enablement

About this role

Kenvue is currently recruiting for a:

Principal, R&D Digital Enablement What we do At  Kenvue , we realize the extraordinary power of everyday care. Built on over a century of heritage and rooted in science, we’re the house of iconic brands - including NEUTROGENA®, AVEENO®, TYLENOL®, LISTERINE®, JOHNSON’S® and BAND-AID® that you already know and love. Science is our passion; care is our talent.

Who We Are Our global team is ~ 22,000 brilliant people with a workplace culture where every voice matters, and every contribution is appreciated.   We are passionate about insights, innovation and committed to delivering the best products to our customers. With expertise and empathy, being a Kenvuer means having the power to impact millions of people every day. We put people first, care fiercely, earn trust with science and solve with courage – and have brilliant opportunities waiting for you! Join us in shaping our future–and yours. For more information , click here .

Role reports to: Assoc Director, R&D Digital Capabilities Location: North America, United States, New Jersey, Summit Work Location: Hybrid What you will do The Principal, R&D Digital Enablement will accelerate product development by identifying and deploying materials informatics and automation solutions to scientific and business problems. Combining chemistry and product-development expertise with data-science literacy and business-analysis skills, this individual will assess opportunities, define requirements, connect R&D teams with technical experts, guide implementation and adoption, and demonstrate measurable value. The role applies and guides computational modeling approaches — from statistical and mechanistic models to cheminformatics and machine learning — partnering with specialists for deep model development while remaining accountable for scientific framing, model fitness, and interpretation.  With appropriate attention to scientific, regulatory, quality, and compliance standards.

Key Responsibilities Identify and Apply Scientific Modeling and Materials Informatics Solutions • Apply systems thinking and structured problem framing to identify product-development problems that could be addressed through materials informatics methods—including predictive modeling, machine learning, simulation, and advanced analytics—or through decision-support tools, workflow automation, and simpler digital interventions.

• Evaluate materials informatics and automation opportunities based on the scientific question, data readiness, workflow maturity, integration needs, technical feasibility, validation requirements, adoption considerations, risk, and expected scientific and business value.

• Use business-analysis practices to define scientific and business problems, map current processes and decision points, develop use cases and requirements, identify data dependencies, establish success measures, and plan value realization.

• Partner with data scientists, computational scientists, chemists, engineers, Tech & Data, external partners, and R&D leadership to evaluate solution options and deploy scalable, supportable capabilities.

• Guide implementation, adoption, and value measurement for selected solutions, ensuring they remain scalable, supportable, and aligned with scientific and business needs.

Enable Product Development Excellence • Embed within R&D project teams and use chemistry and product-development expertise to understand scientific challenges, development risks, experimental workflows, and decision points where computational methods or automation could improve outcomes.

• Improve experimental efficiency, prediction quality, first-time-right execution, cycle time, knowledge reuse, and evidence-based decision-making across new product development programs, deepening formulation and process understanding through structure–property relationships, ingredient compatibility and interaction, stability prediction, and formulation optimization.

• Support teams from concept generation through commercialization by matching scientific and business needs with fit-for-purpose solutions, ranging from workflow automation and analytics to predictive models, machine learning, simulation, and decision-support tools.

Shape Materials Informatics and Automation Opportunities • Lead opportunity discovery, problem definition, data-readiness assessment, technical feasibility evaluation, and evidence-based prioritization for potential computational and automation solutions.

• Determine when a problem calls for mechanistic or statistical modeling, cheminformatics, simulation, machine learning, advanced analytics, decision support, or simpler workflow automation, avoiding unnecessary technical complexity.

• Translate scientific problems into clear use cases and partner with technical experts who design, build, validate, and maintain predictive models, simulations, machine-learning solutions, and other computational capabilities.

• Translate product, consumer, healthcare professional, business, and market needs into well-defined scientific problems, computational use cases, data requirements, solution requirements, and implementation plans.

• Guide responsible technology exploration and disciplined learning cycles, including appropriate consideration of data quality, model performance, explainability, validation, monitoring, change control, and human oversight.

Strengthen Data and Knowledge Foundations • Assess and improve data capture, data quality, metadata, documentation, and contextual information needed to support reliable analysis, automation, and computational modeling.

• Support platforms, metadata, documentation, governance, and stewardship practices that make scientific data, models, methods, assumptions, results, and technical knowledge reliable, traceable, discoverable, and reusable across projects and functional teams.

Drive Adoption and Organizational Change • Lead change and adoption by aligning scientific, technical, business

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