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Tier-1 brand plus mid-level generalist ML role and metro location create high candidate competition.
Strong biopharma and clinical trial data requirements limit transferability across industries.
Explicit 5+ years, clinical/regulatory domain experience, and specific MLOps/LLM tech make filters highly stringent.
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Design, develop, and deploy advanced ML/AI predictive models and statistical analyses focused on clinical development challenges including clinical trial data, EHR/EMR, and RWE/RWD.
Implement AI/GenAI solutions such as LLMs, RAG frameworks, and agentic AI architectures to augment clinical workflows and decision-making under senior guidance.
Collaborate with cross-functional clinical and medical teams to align project objectives, present data insights, and ensure reliable data pipelines with MLOps and engineering best practices.
Bachelor's, Master's, or Ph.D. in Data Science, Statistics, Biostatistics, Computer Science, or related discipline.
5+ years progressive experience in data science or ML engineering with exposure to biopharma, pharma, or clinical research.
Strong proficiency in Python, PySpark, or R; hands-on experience with ML frameworks and clinical trial statistical methodologies.
Experience with clinical trial data, EHR/EMR, RWE/RWD, and working knowledge of biopharma drug development lifecycle and regulated data governance.
Experienced individual contributor comfortable operating independently on complex clinical data science problems with strong scientific judgment.
Proficient in advanced AI/GenAI technologies including practical use of LLMs, prompt engineering, and deployment of agentic AI in clinical or scientific contexts.
Capable of bridging technical data science with clinical stakeholders, communicating complex insights clearly and contributing to scalable, maintainable analytical pipelines and workflows.