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Tier-1 brand, popular Data Scientist title, mid-level experience, and metro location increase applicant competition.
Requires clinical trial, RWE, and HIPAA-regulated experience, limiting cross-industry transferability.
Explicit 5+ years plus mandatory clinical/pharma, MLOps, and GenAI requirements make filters stringent.
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Design, develop, and deploy advanced machine learning and statistical models addressing clinical development challenges including trial data and real-world evidence.
Build and maintain data and analytics pipelines ensuring efficiency and scalability while collaborating with clinical and operational stakeholders to translate data into actionable insights.
Design and implement AI/GenAI solutions such as LLMs and Agentic AI to augment clinical workflows, applying prompt engineering and staying current with emerging AI/ML methodologies.
5+ years of progressive experience in data science or ML engineering, preferably with exposure to biopharma, pharma, or clinical research.
Proficiency in Python, PySpark, or R and experience with clinical trial data, EHR/EMR, or real-world data sources.
Bachelor's, Master's, or Ph.D. in Data Science, Statistics, Biostatistics, Computer Science, or related discipline.
Experience with cloud platforms (AWS/Azure), big data technologies (Spark), MLOps/GitOps practices, and version control tools (GitHub).
Experienced individual contributor who can independently handle complex clinical data science projects with sound scientific judgment.
Strong domain knowledge of biopharma drug development lifecycle, clinical trial methodologies, and data standards within regulated environments.
Practical experience applying AI/GenAI technologies (LLMs, RAG, Agentic AI) in clinical settings and familiarity with data governance, model explainability, and compliance considerations.