Director, R&D – Digital Transformation

Job Description Job Purpose    ​ ​ The   Director, R&D - Digital Transformation   is a senior   leader responsible for defining the digital transformation strategy for the   NA   R&D organization and building the capabilities required to deliver it. This is a greenfield role , as   the Director will   establish   the vision, design the operating model, and develop the target-state organizational structure and multi-year resourcing plan needed to scale a high-performing, multidisciplinary Digital R&D function. The role   will have   three to four direct reports   to support our digital transformation agenda , with organizational design and hiring owned directly by this leader.   In practice, the Director leads a centralized Digital   NA   R&D Hub ,   partnering with   the NAZ Platforms,   R&D   and IT   teams to deploy predictive models, AI-enabled tools, advanced data architectures, and digital twin capabilities that accelerate innovation, reduce physical iteration, and shorten time-to-market.   This individual will drive the definition,   development   and adoption of the new digital workflows and ways of working for the R&D Organization.   This leader sits at the intersection of food and materials science,   process and packing engineering,   data science, and manufacturing   and will closely collaborate across functions and disciplines to bring the transformation agenda to reality.     ​     Essential   Functions &   Responsibilities     ​ ​ Own the R&D digital transformation agenda. Translate organizational ambition into a phased, prioritized strategic roadmap ,   defining required capabilities, sequencing investments, and securing senior leadership alignment and resources. Connect the roadmap directly to enterprise value: top-line growth,   productivity ,   sustainability commitments, and time-to-market competitiveness.     ​ Own   organizational design for the   NA   Digital R&D   team . Define the target-state team structure, capability architecture, and workforce plan   required   to   operate   at scale.   Determine   the   optimal   mix of internal talent, external hires, and strategic partners. Establish hiring profiles and development pathways for a multidisciplinary team spanning data science, ML engineering, and R&D domain   expertise , including but not limited to Product Development, Consumer Science, Process and Package Development.   ​ Lead integration of legacy PLM, ELN, and LIMS systems into a cohesive data platform   in partnership with R&D Transformation.   Define governance protocols ensuring ingredient specifications, packaging tolerances, sensory data, and process parameters share a universal taxonomy. Establish the data quality standards and ingestion pipelines   required   to power reliable AI and predictive analytics at scale.     ​ Lead development and deployment of physics /engineering /chem -based simulation, machine learning, and generative AI models that predict   consumer acceptance/behaviors,   formulation   performance, process behavior, shelf-life, and packaging integrity prior to physical trials. Define   MLOps   infrastructure and model governance practices to move from proof-of-concept to production-grade tools.   Guide build-vs-buy   decisions for digital R&D platforms   in partnership with IT     ​ Design and operationalize a scalable Hub-and-Spoke engagement model enabling rapid, repeatable adoption of digital tools across   Central and Platform   R&D teams. Establish workflows, decision rights, and governance mechanisms to support effective collaboration across geographies and disciplines.     ​ Recruit, develop, and   retain   professionals who can translate between scientific problem statements and digital solutions. Champion digital fluency across the broader R&D organization through training, embedded Spoke partnerships, and storytelling that connects digital tools to commercial outcomes.   ​ Serve as the senior advocate for digital R&D transformation. Build cross-functional coalitions with R&D, Operations, IT, Marketing, and Quality. Drive adoption through proof-of-concept delivery, measurable value creation, and consistent executive engagement.   ​   People Management Responsibilities   Team (salaried ):   2   to   4   direct reports .   Key Outputs & Deliverables      Innovation Velocity & Market Impact    Time-to-Market Reduction: Decrease in   idea -to-commercialization cycle time for new product development and packaging transitions . Reduce number of line trials and associated   cost .   Agile Sprint Velocity: Digital innovation sprints completed per quarter resulting in   a viable   commercial brief   Right- First-Time : Increase overall quality of delivery of projects, measured as % of right   first time   initiatives to market (no intervention needed after launch)    Organizational Design & Capability Build     Org Design Completion: Approved future-state organizational structure and resourcing roadmap delivered within 12 months   Transformation Roadmap Adoption: Executive-aligned digital R&D strategy approved within 6 months   Resource Efficiency & Predictive Accuracy   Physical Iteration Reduction: Decrease in benchtop iterations or pilot plant /plant trial   runs   required   per project , and associated cost and resource reduction   Model Correlation: Statistical correlation between digital twin/simulation model outcomes and actual physical results   Organizational Adoption & Data Quality   Spoke Utilization Rate: Percentage of active R&D projects incorporating predictive modeling or digital sensory analysis into their stage-gate process.   Data Quality Index: Rolling metric measuring completeness, accuracy, and   standardization of data ingested into the central ontology   Expected Experience   & Required Skills   ​ ​ Bachelor’s degree   in engineering, data science, computer science, food science, or a related technical field   with 17 years of r