Role Description As a core member of a cross‑functional “pod team” — including Product Management and Strategic Marketing — you will help define and execute the strategic innovation vision for a pharma portfolio within a designated market vertical (e.g., Small Molecules, ATMP, Biologics & Vaccines). The pod team operates with shared accountability, ensuring alignment and balanced decision‑making as you collectively shape the innovation pipeline and drive strategic execution. Reporting to the RD&A Director, you will synthesize global market, customer, and competitive insights to guide the long‑term innovation roadmap for your assigned vertical and work closely with global stakeholders to maximize value creation for Kerry. This role requires strong scientific credibility, strategic thinking, and cross‑functional collaboration, regularly interfacing with Product Management, Business Development, Sales, Marketing, Regulatory, Operations, and Quality. You will help guide the product lifecycle, support customer engagements, and influence the strategic direction of Kerry’s pharma innovation platforms. Key Responsibilities - Serve as the subject matter expert for the assigned pharma market vertical, providing deep scientific and application expertise. - Develop and execute the long‑term product and innovation roadmap for assigned technologies. - Assess market trends, unmet needs, and competitive dynamics to identify growth opportunities. - Partner with RD&A and cross‑functional stakeholders to prioritize innovation and renovation projects. - Contribute to business cases and champion new product development initiatives. - Translate complex scientific and technical concepts into clear value propositions and differentiators. - Collaborate with Marketing to develop technical content for digital platforms and key scientific conferences. - Support customer engagements by connecting technical solutions to market needs. - Contribute to strategic business analyses, including market assessments and potential M&A or partnership opportunities. Qualifications - Master’s or PhD in Biochemistry, Biotechnology, Chemical Engineering, Pharmaceutical Sciences, or a related field. - 8+ years of experience in biopharma, drug product development, biologics, vaccines, CGT, excipients, or related technical areas. - Strong understanding of biopharma manufacturing, including biologics, vaccines, CGT, and sterile injectables. - Proven ability to bridge scientific and commercial perspectives. - Excellent communication, project management, and problem‑solving skills. - Experience in customer‑facing technical roles, such as formulation, process development, or field application support. - Familiarity with GMP environments, pharmacopeial standards, and regulatory frameworks. Preferred Qualifications - Background in excipients, stabilizers, buffer systems, or process materials for biologics and CGT. - Up to 15% global travel. Compensation and Benefits - The pay range for this position is $88,100 - $163,700 in Annual Salary. - Kerry offers a competitive benefits package, including: - Medical, dental, vision. - Paid time off. - 401(k) plan with employee and company contribution opportunities. - Employee Share Plan. - Life, disability, and accident insurance. - Tuition reimbursement. Company Description Kerry is the world's leading taste and nutrition company for the food, beverage, and pharmaceutical industries. Every day we partner with customers to create healthier, tastier, and more sustainable products that are consumed by billions of people across the world. Our vision is to be our customers' most valued partner, creating a world of sustainable nutrition.

Role Description This is an applied science position focused on designing, building, and deploying production-grade document understanding systems that power Westlaw, PracticalLaw, and CoCounsel. You will work across semantic chunking, document enrichment, and knowledge graph construction for complex legal, tax, and accounting content — delivering foundational intelligence that multiple product teams depend on at scale. Qualifications - PhD or Master's in Computer Science, AI, NLP, or a related field - 5+ years of post-degree industry experience shipping document understanding, information extraction, or knowledge graph systems into production — not research-only experience - Publications at ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD, or equivalent - Experience leading through influence in an applied research setting - Production Python and experience with PyTorch, Hugging Face Transformers, and DeepSpeed Requirements - Hands-on production depth required in: - Document layout analysis and semantic chunking beyond fixed-size or paragraph-based methods - Hierarchical, multi-label document classification with domain-specific and customer-defined schemas - Entity recognition and linking, relation extraction, citation parsing, and knowledge graph construction from unstructured text - LLM-based information extraction, few-shot and multi-task learning, and post-training - Knowledge distillation, model compression, and SLM deployment under latency constraints - Synthetic data generation for NLP: query-answer generation with verification and scalable data augmentation - Annotation workflow design and evaluation framework development for document understanding tasks Benefits - Flexibility & Work-Life Balance: - Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities. - Flexible work arrangements, including work from anywhere for up to 8 weeks per year. - Career Development and Growth: - Culture of continuous learning and skill development. - Grow My Way programming and skills-first approach. - Industry Competitive Benefits: - Comprehensive benefit plans including flexible vacation, mental health days, retirement savings, and tuition reimbursement. - Culture: - Award-winning reputation for inclusion and belonging. - Values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together. - Social Impact: - Two paid volunteer days off annually. - Opportunities for pro-bono consulting projects and ESG initiatives. - Making a Real-World Impact: - Helping customers pursue justice, truth, and transparency.

Role Description The Principal AI Data Scientist – Service will join the Supply Chain AI team, reporting to Director, Supply Chain AI Technology. This role is responsible for designing, developing, and deploying end-to-end AI and data science solutions that power our global service operations—spanning field service engineer (FSE) workforce optimization, service job routing, predictive and preventative maintenance, and service parts inventory management. This role is ideal for senior practitioners who are passionate about building production-grade, cloud-native AI/ML and optimization solutions—not just proofs of concept. You will leverage Azure for compute and agent workflows, and Snowflake as the enterprise data platform, to deliver scalable, reliable, and secure solutions with robust CI/CD. You will also work closely with service leaders, planners, and operational teams to translate business problems into AI solutions that drive measurable impact. This role will be working remotely and expected to travel up to 25-30% for workshop and stakeholder engagement. In this role, you will have the opportunity to: - Design, develop, and deploy end-to-end AI and data science solutions for service use cases such as: - Build, productionize, and maintain ML/OR models and agentic workflows using Azure services (e.g., Azure Functions, Container Apps/Kubernetes, Azure ML, Event-driven architectures) and Snowflake as the core data platform. - Own the full lifecycle from problem framing, data exploration, feature engineering, modeling, and experimentation to deployment, monitoring, and continuous improvement in a CI/CD environment (e.g., Azure DevOps or GitHub Actions). - Collaborate cross-functionally with Service Operations, Field Service, Parts Planning, IT, and product/engineering teams to align technical solutions with operational realities, SLAs, and business KPIs. - Partner with solution architects, data engineers, and platform engineers to define robust data models and data pipelines in Snowflake that support analytics, optimization, and real-time decisioning at scale. Ensure models and services are robust, secure, and production ready, with clear monitoring, alerting, feedback loops, and retraining strategies. Qualifications - PhD or Master’s degree in Data Science, Computer Science, Operations Research, Industrial Engineering, Applied Mathematics, or a related quantitative field. - Multiple years of experience (typically 7+ years) in AI, data science, or advanced analytics, with significant experience focused on service, maintenance, field operations, or closely related operational optimization domains. - Strong proficiency in Python and experience with modern ML/AI and optimization frameworks (e.g., scikit-learn, PyTorch, OR-Tools/Gurobi or similar, and optionally LLM/agent frameworks for workflow orchestration). - Demonstrated experience building and deploying production-grade AI/ML or optimization solutions on Azure, including containerized services, APIs, and CI/CD pipelines. - Hands-on experience working with Snowflake (or a similar cloud data platform) for data modeling, SQL development, performance tuning, and integrating analytics/ML workloads. Requirements - Direct experience in Service Operations, Field Service, Asset Management, or Service Supply Chain functions. - Familiarity with ERP and service management systems (e.g., Oracle, SAP, ServiceMax, Salesforce Field Service, or similar) and how they support service orders, assets, and parts. - Experience with large language models (LLMs) and intelligent agents applied to service workflows, such as technician copilots, knowledge retrieval, or conversational troubleshooting. Benefits - Comprehensive package of benefits including paid time off. - Medical/dental/vision insurance. - 401(k) to eligible employees.

• Develop predictive and machine learning models to solve business problems (churn, demand forecasting, segmentation, etc.) • Conduct advanced statistical analyses and controlled experiments (A/B tests, causal inference) • Perform exploratory data analysis to identify patterns, trends and opportunities • Communicate results and recommendations clearly to technical and non-technical stakeholders • Define evaluation metrics and monitor model performance in production • Act as a technical reference in data science, guiding best practices and methodologies • Collaborate with data engineers to ensure the availability and quality of required data