Principal, R&D Digital Enablement

Kenvue•Summit, NJ

8d•Hybrid

About The Position

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.

Requirements

Bachelor's degree or higher in Chemistry, Chemical Engineering, Pharmaceutical Sciences, Materials Science, or a closely related discipline with substantial chemistry content; formal education or demonstrated applied training in data science, statistics, cheminformatics, computational science, or a related quantitative field.
6+ years of progressive experience in chemistry-based product development, formulation, process development, computational or data-enabled R&D, or digital enablement, including hands-on delivery of consumer health, OTC, personal care, cosmetics, medical device, pharmaceutical, or related products.
Strong systems-thinking and problem-framing skills, with the ability to connect strategy, processes, data, technology, people, and business outcomes.
Experience with materials informatics in product or process development, including applications such as cheminformatics, computational chemistry, formulation informatics, design of experiments, multivariate analysis, predictive modeling, simulation, optimization, or machine learning, preferably in a regulated or quality-managed environment.
Experience using business-analysis methods to frame scientific problems, map processes and decisions, define materials informatics or automation use cases, identify data and integration requirements, assess options, and support requirements definition, user acceptance, implementation, adoption, and outcome measurement with technical delivery teams.
Demonstrated data-science literacy, including the ability to evaluate data fitness, understand common analytical and machine-learning approaches, discuss model inputs and outputs with technical specialists, and recognize limitations, uncertainty, bias, and validation needs.
Hands-on experience applying computational or statistical models to chemistry, formulation, or process problems; deep specialist model development is supported by technical partners.
Effective communication and leadership presence, with the ability to explain complex scientific and digital concepts, facilitate decisions, and engage senior leaders.
Demonstrated learning agility, self-direction, and growth potential in rapidly evolving technical and organizational environments.
Familiarity with regulatory requirements and quality standards applicable to scientific research, product development, and data management.

Nice To Haves

Experience shaping or delivering laboratory informatics, data, knowledge-management, or enterprise digital roadmaps.
Experience applying continuous-improvement, Lean Product Development, Six Sigma, process excellence, or related methods to scientific workflows.
Experience building enterprise capability and adoption programs using communities of practice, reusable learning resources, metadata, structured content, enterprise search, data governance, or AI-ready knowledge strategies.
Experience developing business cases, outcome measures, or value-realization frameworks for digital investments.
Demonstrated motivation to bridge R&D domain expertise with emerging digital capabilities and continuously expand technical breadth.

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, and leadership stakeholders; communicating clearly; providing hands-on support; and reinforcing new ways of working. Develop training materials, best practices, communities of practice, and reusable playbooks that build sustainable capability. Coach scientists and engineers on effective use of computational models, AI, analytics, automation, and digital tools, including how to interpret outputs, recognize limitations, and maintain appropriate human judgment. Communicate with clarity and executive presence, adapting complex scientific and digital concepts for different audiences.
Measure Value Creation: Define and track adoption, scientific outcomes, and business impact before and after deployment, including experimental efficiency, cycle-time reduction, prediction accuracy, decision quality, knowledge reuse, quality, user adoption, and project-execution performance. Develop value-realization approaches that connect digital investments to strategic priorities and measurable R&D outcomes. Communicate recommendations, trade-offs, outcomes, and success stories to R&D and enterprise leadership.