Head of Process Analysis & Adoption

Role Description

Senior Principal Scientist -  Head of Process Analysis & Adoption (PAA) - VMRD Automation & Data Sciences

Zoetis is the global leader in animal health, manufacturing innovative pharmaceutical products across the full spectrum of veterinary care. To secure our future and accelerate innovation, we are advancing bold, enterprise-wide data and digital transformation, empowering faster decisions, and better solutions for animals worldwide.

Within Veterinary Medicine Research & Development (VMRD), we are reimagining how science is done in a digital-first world. By executing our data and digital strategy, we will unlock new value for our colleagues, Zoetis, and our customers. It is an exciting time to join the Automation and Data Sciences (ADS) group in VMRD to lead this transformation.

Position Summary

In close partnership with scientific, clinical, and regulatory stakeholders, this role will lead a team that evaluates, optimizes, and drives adoption of cross-functional R&D processes to accelerate discovery through development and serve as the connection between scientists, ADS technical teams, Zoetis Tech & Digital (ZTD), and external services.

The role owns the end-to-end process improvement and change adoption portfolio, from discovery and analysis through solution definition, delivery readiness, training, and benefits realization, ensuring solutions are business-led, user-centered, and measurably improve cycle time, quality, compliance, and reuse of scientific data. The ideal candidate blends process excellence and change leadership with business analysis rigor, translating lab and study needs into actionable requirements and adoption plans at scale.

This role emphasizes leadership in collaboration, change management, and cross-functional innovation to enhance Zoetis' ability to deliver innovative therapeutics, vaccines, biodevices, and diagnostics.

Essential Duties and Responsibilities

• Evolve and lead the PAA team (process analysts, change/adoption specialists, and business analysts), fostering a culture of curiosity, delivery excellence, and continuous improvement.
• Define team operating norms, roles, responsibilities, and quality standards; manage PAA capacity, assignments, and workload across initiatives.
• Own the VMRD process analysis and adoption portfolio: prioritize opportunities with scientific leaders; define scope, success metrics, and sequencing; manage benefits tracking and reporting.
• Lead structured process discovery and optimization with an Agile methodology focus:
  • Define epics and user stories, mapping current/future states, quantifying constraints, and designing right-sized interventions.
  • Ensure relevant source materials (e.g., SOPs, templates, worksheets, reports, system references) are gathered before workshops and discovery sessions; distinguish what can be learned from documentation vs. direct stakeholder engagement.
  • Establish a practical adoption planning approach aligned with product discovery, delivery, release, and post-launch support; ensure post-launch feedback is captured and used to improve adoption and future iterations.
  • Identify impacted stakeholder groups, readiness concerns, adoption barriers, training needs, and communication needs early enough to influence product and delivery decisions.
• Drive cross-use-case pattern recognition so process learnings and adoption insights are reusable across products and future workstreams.
• Drive business analysis for digital and lab automation initiatives:
  • Elicit, synthesize, and validate user stories and acceptance criteria. Ensure that acceptance criteria align with FAIR principles, where pertinent. Partner with internal ADS product owners, data/automation engineers, ZTD, and external implementation partners (technical consultants and product leads) to shape feasible, scalable solutions and ensure clear delineation of responsibilities between internal teams and vendor resources. Partner with business leads to define Key Performance Indicators to quantify, where practical, the benefits of digital investments.
• Orchestrate adoption and change management:
  • Create stakeholder analyses, communications, training curricula, and hypercare plans.
  • Establish and run communities of practice and role-based enablement.
  • Monitor adoption KPIs (e.g., activation, engagement, task success) and remove barriers to usage.
• Establish and maintain standard PAA templates, methods, and deliverables (e.g., stakeholder maps, data source capture templates, process documentation, adoption plans, readiness assessments, risk logs, feedback summaries); ensure consistent use and storage in the agreed source of truth.
• Define and implement success metrics and measurement plans for each initiative (e.g., cycle time deltas, documentation touch time, error/redo rates, compliance conformance, data completeness/lineage).
• In coordination with Quality and Compliance, ensure processes and supporting digital solutions meet relevant regulatory and quality expectations (e.g., GxP contexts, data integrity) where applicable.
• Partner with program management to align dependencies, risks, and milestones; ensure readiness for deployment (process, people, and data) and smooth handoffs to operations.
• Maintain a living library of instructional materials supporting digital initiatives in the appropriate location to ensure access, continuity and audit compliance. Scan industry trends and benchmark best practices in lab digitalization, data capture/visualization, and scientific workflow optimization; pilot and scale high-ROI innovations.
• Coach and mentor PAA specialists and cross-functional colleagues; elevate organizational capabilities in process thinking, requirements discipline, and adoption planning.

Requirements

• Education: Bachelor’s degree in a relevant field (e.g., life sciences, engineering, operations research, information systems, or related). Advanced degree preferred.
• Experience:
  • 10+ years in process analysis/improvement and adoption within life sciences or adjacent regulated R&D environments; 3+ years in a people-leadership role.
  • 5+ years applying Agile methodologies in an R&D product/platform context, including defining epics and user stories, writing acceptance criteria, partnering with product owners, and maintaining backlog hygiene to drive iterative delivery.
  • Demonstrated delivery of multi-team process/digital transformations from discovery through adoption with quantifiable business outcomes.
  • Hands-on business analysis experience - requirements elicitation, process/data modeling, and backlog refinement; proficient in defining epics, user stories, and acceptance criteria.
• Methodologies and Skills:
  • Strong command of Agile/Scrum (user stories, backlog refinement, acceptance criteria), familiarity with Waterfall/SDLC, proficiency in requirements elicitation and documentation (BRDs/FRDs, BPMN/UML, use cases), process mapping , stakeholder management and facilitation (workshops, interviews), testing support (UAT planning/execution, defect triage).
  • Exceptional stakeholder engagement, facilitation, and communication skills; able to translate between scientific and technical domains and influence without authority.
  • Strong change management toolkit (e.g., PROSCI/ADKAR or equivalent) and learning design principles for training and enablement.
  • Experience using facilitation and documentation tools (e.g., Miro, structured discovery templates, process maps, stakeholder maps, journey maps, adoption plans, shared documentation repositories).
  • Familiarity with scientific workflows and tools (e.g., material registration, ELN/LIMS, instrument data capture, data analysis lab automation) and FAIR data practices supporting use of these workflows and tools. Analytical rigor with comfort in defining practical KPIs, baselines, and benefits realization based on available data; fluency with process mapping and requirements tools.
  • Experience with Lean/Six Sigma; certification preferred.
  • Familiarity with data products, analytics/reporting workflows, source system discovery, data capture templates, metadata, data quality and lineage, and source-of-truth concepts.
• Work style:
  • Organized, resilient, and delivery-focused; adept at navigating ambiguity, managing multiple initiatives, and operating across matrixed teams.

 

 

Full time

 

 

Regular

 

 

Colleague

 

 

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