Director, R&D – Digital Transformation

Job Purpose

TheDirector R&D - Digital Transformationis a seniorleader responsible for defining the digital transformation strategy for theNAR&D organization and building the capabilities required to deliver it. This is a greenfield role asthe Director willestablishthe 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 rolewill havethree to four direct reportsto support our digital transformation agenda with organizational design and hiring owned directly by this leader.

In practice the Director leads a centralized DigitalNAR&D Hubpartnering withthe NAZ PlatformsR&Dand ITteams 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 definitiondevelopmentand 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 scienceprocess and packing engineeringdata science and manufacturingand will closely collaborate across functions and disciplines to bring the transformation agenda to reality.

EssentialFunctions &Responsibilities

• Own the R&D digital transformation agenda. Translate organizational ambition into a phased prioritized strategic roadmapdefining required capabilities sequencing investments and securing senior leadership alignment and resources. Connect the roadmap directly to enterprise value: top-line growthproductivitysustainability commitments and time-to-market competitiveness.

• Ownorganizational design for theNADigital R&Dteam. Define the target-state team structure capability architecture and workforce plantooperateat scale.Determinetheoptimalmix 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 domainexpertise 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 platformin 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 pipelinesto 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 predictconsumer acceptance/behaviorsformulationperformance process behavior shelf-life and packaging integrity prior to physical trials. DefineMLOpsinfrastructure and model governance practices to move from proof-of-concept to production-grade tools.Guide build-vs-buydecisions for digital R&D platformsin partnership with IT

• Design and operationalize a scalable Hub-and-Spoke engagement model enabling rapid repeatable adoption of digital tools acrossCentral and PlatformR&D teams. Establish workflows decision rights and governance mechanisms to support effective collaboration across geographies and disciplines.

• Recruit develop andretainprofessionals 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):2to4direct reports.

Key Outputs & Deliverables

• Innovation Velocity & Market Impact

• Time-to-Market Reduction: Decrease inidea-to-commercialization cycle time for new product development and packaging transitions. Reduce number of line trials and associatedcost.

• Agile Sprint Velocity: Digital innovation sprints completed per quarter resulting ina viablecommercial brief

• Right-First-Time: Increase overall quality of delivery of projects measured as % of rightfirst timeinitiatives 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 trialrunsper 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 andstandardization of data ingested into the central ontology

Expected Experience& Required Skills

• Bachelors degreein engineering data science computer science food science or a related technical fieldwith 17 years of relevant experienceORMasters degreewith 16 years of relevant experienceORPhD with 15 years of relevant experience

• 12 years leading digital analytics or advanced technology initiatives within R&D manufacturing or product development withdemonstratedgrowth in scope and organizational impact

• Proventrack recorddefining not just executingdigitaltransformation strategy in a complex matrixedCPGor food & beverage environment

• Demonstrated experience in organizational design and workforce planning: building team structures hiring roadmaps and scaling functions fromearly stageto operational maturity

• Experience managingand interacting inmultidisciplinary teams

• Deep technical fluency inone or more of thefollowing:food science product development chemical/mechanicalengineering and/or packaging development

• Strong foundational knowledge of machine learning generative AI computational modeling and modern data architecture (data lakes data meshMLOps)

• Familiarity with R&D enterprise systems (PLM ELN LIMS) and associated data interoperability challenges

• Ability to define a compelling transformation vision and connect R&D capability directly to enterprise value

• Skilled at navigating legacy corporate cultures aligning cross-functional stakeholders andestablishingclear decision rights over data governance and digital protocols

• Entrepreneurial orientation: comfortable with ambiguity building from scratch and making sound decisions with incomplete information

• Executive presence sufficient to influence at thesenior executivelevel and represent the digital R&D agenda with external partners and industry peers

Work Environment & Schedule

This positionoperatesprimarily in a hybrid work environment and is based at the Kraft Heinz R&D Innovation Centerlocatedin Glenview IL. Our hybrid model provides flexibility based on business needs allowing employees to work virtually up to two days per week with an expectation to work on-site at least three days per week at the Glenview Innovation Center.

Offering Sponsorship:Wereable to offer sponsorship for this role.