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Tier-1 brand, mid-level popular title, metro location, and broad ML/GenAI skillset.
Requires clinical trial, RWD/RWE, and HIPAA/regulatory knowledge, limiting cross-industry transferability.
Explicit 5+ years, biopharma/regulatory experience, and mandated GenAI/MLOps skills increase filter strictness.
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Design, build, and deploy advanced ML and AI solutions focusing on clinical development challenges using methods like regression, clustering, survival analysis, and time series forecasting.
Conduct rigorous statistical analyses on clinical trial data, EHR/EMR, and real-world evidence to provide actionable insights and support clinical workflows.
Implement MLOps/GitOps practices to maintain models and pipelines ensuring quality, versioning, and scalability while collaborating with cross-functional clinical and data teams.
5+ years of progressive data science or ML engineering experience with exposure to biopharma, pharma, or clinical research environments.
Bachelor's, Master's, or Ph.D. in Data Science, Statistics, Biostatistics, Computer Science, or related fields.
Proficiency in Python, PySpark, or R; experience with clinical trial data, EHR/EMR, and real-world data sources relevant to clinical development.
Practical experience with AI/GenAI technologies including LLMs, RAG frameworks, Agentic AI, and prompt engineering in production or near-production settings.
Experienced individual contributor with demonstrated delivery of end-to-end analytics or ML solutions in clinical/pharma contexts, capable of independent work on complex problems.
Familiar with clinical trial data standards, biopharma drug development lifecycle, and regulated data governance environments.
Skilled in both analytical/statistical methods and engineering practices including MLOps, cloud platforms (AWS/Azure), and big data tools, showing a balanced focus on scientific rigor and scalable implementation.