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Tier-1 employer, mid-level ML role, metro location, and popular GenAI title increase candidate competition.
Strong ML skill transferability but preference for pharma/supply-chain regulated experience increases domain specificity.
Explicit 5+ years, specialized GenAI/LLM skills, and regulated environment preference enforce strict technical filters.
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Design, develop, and deploy complex AI/ML solutions, especially Generative AI and Large Language Models, to optimize global supply chain clinical and commercial processes.
Own end-to-end AI data science projects from problem framing through deployment and stabilization, focusing on measurable outcomes like cycle time, forecast quality, and operational efficiency.
Collaborate cross-functionally with business, technology, and AI platform teams to integrate AI capabilities into supply chain applications and workflows for real-time intelligence and automation.
5+ years of experience developing AI/ML and Generative AI solutions, specifically applying Large Language Models and advanced modeling for supply chain use cases.
Hands-on skills in Python, AI/ML libraries, APIs, SQL/NoSQL, and AI orchestration frameworks such as LangChain or similar.
Experience building GenAI applications including chatbots, Retrieval-Augmented Generation pipelines, vector DB integration, and prompt-driven AI workflows.
Prior exposure to pharma, life sciences, or regulated AI environments preferred; knowledge of GxP, HIPAA, or compliance is a plus.
Experienced applied AI data scientist with deep expertise in Generative AI, LLMs, and AI orchestration aiming to solve complex supply chain problems.
Comfortable operating within highly regulated, pharma or life sciences contexts with awareness of compliance and enterprise AI governance.
Skilled at bridging technical solutions with business needs through clarity in communication, problem-solving, and Agile delivery in large cross-functional teams.