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Tier-1 brand and metro location increase interest, but niche LLMOps and GxP requirements limit qualified applicants.
Strong domain specificity (clinical submissions, GxP, regulatory data) makes cross-industry transferability low.
Requires deep specialized ML/AI, LLMOps, and regulated GxP clinical-submissions experience, so screening will be strict.
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Lead end-to-end development and deployment of data-driven AI/ML models for clinical data and regulatory submission assets.
Build advanced generative AI agents and bespoke multi-agent workflows (e.g., LangGraph, GraphRAG) to support clinical data scientists in a GxP regulated environment.
Drive medium-sized projects that align with business strategy and significantly influence clinical data quality and authoring timelines.
Proven experience independently owning data science projects from inception to deployment, influencing medium-sized business decisions.
Proficiency in Python (AI/ML backend/agent logic) and R (clinical statistical programming), familiarity with SAS is a plus.
Hands-on expertise with LangChain, LangGraph, AWS AgentCore, and advanced RAG methodologies including chunking, embeddings, GraphRAG.
Ability to communicate complex analytical findings clearly to technical and non-technical stakeholders.
Experienced in regulated GxP clinical or pharmaceutical R&D data environments with strong domain and technical expertise in advanced generative AI and multi-agent architectures.
Technically versatile with mastery over multiple programming languages, AI/ML frameworks, and advanced data visualization/statistical modeling.
Capable of independently driving medium-sized strategic analytical projects that improve clinical data content management and regulatory submission processes.