Principal, R&D Digital Enablement

Kenvueは現在、a:

Principal, R&D Digital Enablement

Kenvueで、日常のケアの並外れた力を実感します。100年以上にわたる伝統を基盤に、科学に根ざした当ホテルは、ニュートロジーナ®、アヴェーノ®、タイレノール®、リステリン®、ジョンソンズ®、バンドエイド®など、すでにお馴染みのアイコニックなブランドを展開しています。科学は私たちの情熱です。ケアは私たちの才能です.

私たちのグローバルチームは~22,000人の優秀な人々で、すべての声が重要で、すべての貢献が評価される職場文化を持っています。私たちは洞察に情熱を注いでいます。 革新とお客様に最高の製品を提供することに取り組んでいます。専門知識と共感力を持つKenvuerであることは、毎日何百万人もの人々に影響を与える力を持つことを意味します。私たちは人を第一に考え、熱心に気を配り、科学で信頼を勝ち取り、勇気を持って解決します。そして、素晴らしい機会があなたを待っています!私たちと一緒に、私たちの、そしてあなたの未来を形作りましょう。詳細については をクリックしてください here.

Assoc Director, R&D Digital Capabilities

North America, United States, New Jersey, Summit

ハイブリッド

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.

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…