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Strong employer brand, popular ML role, mid-level experience, and metro location increase candidate competition.
Requires clinical/CDISC and GxP expertise plus specialized LLMOps, making cross-industry transferability low.
Multiple mandatory niche technical skills, regulatory GxP requirements, and explicit experience make screening highly strict.
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Lead design and development of advanced AI multi-agent systems to automate clinical data programming and regulatory submission processes.
Develop and maintain production-grade end-to-end AI pipelines for clinical trial data mapping and statistical code generation under GxP regulatory conditions.
Drive strategic AI initiatives by creating continuous learning systems with human-in-the-loop feedback, improving clinical development timelines.
5 to 7+ years leading complex data science/AI engineering projects end-to-end.
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Physics, or related quantitative field.
Proven expertise building multi-agent AI architectures and production-grade retrieval-augmented generation (RAG) systems beyond simple LLM API use.
Hands-on experience with Python and R; familiarity with clinical statistical programming, CDISC standards (SDTM, ADaM), and working in regulated GxP environments.
Experienced in deploying AI solutions specifically within clinical programming and regulatory submission domains adhering to strict regulatory standards.
Technical leadership capability in guiding junior data scientists and managing multiple AI projects simultaneously in agile, cross-functional teams.
Strong strategic orientation towards integrating advanced AI methodologies into enterprise clinical operations with effective communication to mixed technical and non-technical stakeholders.