AI/ML, Generative AI, and advanced modeling delivery: Develop innovative AI/ML solutions using Large Language Models, Generative AI, foundation models, supervised and unsupervised learning, and other advanced modeling techniques to support supply chain decision-making and automation.
AI application integration and automation: Integrate AI capabilities into Global Supply Chain applications, APIs, workflows, assistants, copilots, and automation platforms using context engineering, tool integration, and emerging approaches such as Model Context Protocol to enable real-time intelligence, productivity, operational efficiency, and improved user experience.
End-to-end AI and data science ownership: Own complex AI, data science, and decision-support solutions from problem framing through prototype, validation, deployment, and stabilization, managing scope, risks, dependencies, timelines, technical tradeoffs, and measurable outcomes such as cycle time, forecast quality, data quality, and operational efficiency.
Technical documentation, quality, and problem-solving: Create clear documentation for prototypes and solutions, including design choices, data flows, AI workflows, assumptions, limitations, and key implementation details; identify and resolve technical challenges effectively.
Technology evaluation and learning agility: Stay current with emerging AI technologies, frameworks, industry trends, and engineering practices, demonstrating the ability to assess options, conduct due diligence, and make informed technology recommendations based on business value, technical fit, and implementation impact.
Product and module ownership: Develop a strong understanding of the overall product, its modules, dependencies, AI components, and user workflows while serving as a technical expert for assigned components or solution areas.
Cross-functional collaboration and stakeholder partnership: Work closely with product teams, business teams, technology teams, architecture teams, AI platform teams, data teams, stakeholders, and subject-matter experts to deliver practical, scientific, data-driven solutions aligned with enterprise standards.
Requirements translation and Agile delivery: Synthesize business, scientific, and technical inputs into prioritized features, user stories, acceptance criteria, and delivery plans; actively participate in Agile ceremonies, including sprint planning, backlog refinement, estimation, demos, and retrospectives.
Innovation and continuous improvement: Contribute to a culture of accountability, continuous learning, innovation, technical curiosity, rapid experimentation, platform-first thinking, and high-quality delivery.
Must-Have Skills
Experience developing and applying AI/ML, Generative AI, analytics, automation, BI, and data visualization solutions to supply chain use cases, using techniques such as Large Language Models, foundation models, predictive modeling, feature engineering, and exploratory data analysis to solve complex business problems.
Hands-on experience building GenAI and LLM-based applications, including chatbots, copilots, Retrieval-Augmented Generation pipelines, vector database integration, prompt-driven workflows, context-aware AI systems, and enterprise application or workflow integration.
Experience with AI orchestration and application frameworks such as LangChain, LangGraph, or similar tools.
Strong understanding of AI agent architectures, orchestration patterns, Responsible AI, model validation principles, and enterprise AI governance.
Strong hands-on development skills for AI and data science solutions, including Python, AI/ML and data science libraries, APIs, SQL/NoSQL databases, data pipelines, and modern application development practices.
Prior exposure to pharma, life sciences, or regulated AI environments preferred; familiarity with GxP, HIPAA, or related compliance expectations is a plus.
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