Head of R&D Data and Digital

Requirements

• Education : Bachelor's Degree in Computer Science, Computer Information Systems, Data Science, and/or equivalent training/experience in a related field.
• Experience : A minimum of 8 years of experience in an information, digital, and/or data technology role delivering and supporting large scale data and digital solutions, with a minimum of 4 years managing people; 3+ years in the health sciences industry is desired.
• Full time
• Regular
• Colleague
• Zoetis will consider any candidate for whom an Agency has submitted an unsolicited resume to have been referred by the Agency free of any charges or fees. This includes any Agency that is an approved/engaged vendor but does not have the appropriate approvals to be engaged on a search.
• Notice: Zoetis Recruiters will contact cand

Benefits

Additional Information

Role Description Position Summary: 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 a 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's an exciting time to join the Automation and Data Sciences (ADS) group in VMRD to lead this transformation. In close partnership with scientific, clinical, and regulatory stakeholders, this role will steward a modern R&D data ecosystem that spans data governance, FAIR data practices, advanced analytics, AI/ML, and digital lab solutions to ensure high‑quality, compliant, and reusable data at scale. Success will be measured by reduced cycle times, higher data quality and traceability, increased reuse of scientific data and models, and clear impact on portfolio decisions and overall R&D productivity. The ideal candidate brings a proven record of delivering complex data and digital platforms and translating scientific needs into secure, scalable, user‑centric solutions that advance our mission to deliver impactful innovations in animal health. Essential Duties and Responsibilities: Recruit, grow, and lead high‑performing teams across data engineering, data analytics, data science, and product management, fostering a culture of accountability, collaboration, and delivery excellence. Own the R&D digital strategy and portfolio, prioritizing investments against scientific objectives to sequence multi‑year initiatives, maximizing value, speed, and compliance. Define and scale the R&D AI and data science strategy, focusing on high‑ROI use cases with clear success criteria, change management, and measurable scientific impact. Accountable to execute on the delivery of $30M+ R&D data and digital programs, while leading cross‑functional teams to deploy complex, end‑to‑end solutions that accelerate discovery through regulatory submission, with disciplined stage gates, risk management, and benefits realization. Own the R&D data and digital roadmap, integrating portfolio priorities with regulatory and privacy obligations (GxP, data integrity, data privacy), establishing a governance model, and ensuring audit readiness across the lifecycle. Govern and enforce a standard data architecture, metadata, and FAIR principles and implement quality controls, lineage, and stewardship to ensure trusted, compliant data at scale. Build and operationalize self‑service data products and insight platforms while championing product‑centric agile delivery with robust user experience, backlog hygiene, and release discipline. Partner with IT to establish a comprehensive operating model for enterprise‑scale R&D platforms and data solutions, including capacity planning, SLAs, incident/problem management, vendor performance, and cost optimization to ensure reliability, scalability, and sustained value. Institutionalize outcome measurement by defining and tracking KPIs and socialize results and lessons learned to drive adoption and continuous improvement. Orchestrate cross‑domain data integration from early discovery through product submission by delivering ELN/LIMS interoperability, automated experiment workflows, and standardized pipelines to improve reproducibility and throughput. Elevate organizational capabilities by launching communities of practice and training curriculum, along with embedding responsible AI practices, enabling scientists to leverage advanced tools confidently and safely.

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