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Mid-level data scientist at a global pharma, metro location and broad skillset create high applicant competition.
Strong clinical trial, biopharma and regulated-data requirements limit cross-industry transferability.
Explicit 5+ years, clinical data/regulatory experience and mandatory ML/MLOps skills make shortlisting highly strict.
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Design, develop, and deploy advanced ML/AI models and statistical analyses on clinical trial, EHR/EMR, and real-world data to support clinical development decisions.
Build and maintain scalable data pipelines and integrate AI/GenAI solutions (including LLMs and Agentic AI) to augment clinical workflows.
Collaborate cross-functionally with clinical, medical, and operational teams, presenting data-driven insights while ensuring adherence to MLOps and engineering best practices.
5+ years of progressive experience in data science or ML engineering with exposure to biopharma, pharma, or clinical research environments.
Proficiency in Python, PySpark, or R with experience in clinical/statistical packages and ML frameworks.
Experience working with clinical trial data, EHR/EMR, or real-world evidence and understanding of clinical trial statistical methodologies.
Bachelor's, Master's, or Ph.D. degree in Data Science, Statistics, Biostatistics, Computer Science, or a related discipline.
Experienced in applying AI/GenAI technologies like LLMs, Retrieval-Augmented Generation, and Agentic AI within clinical or drug development contexts.
Capable of independently managing moderately complex to complex analytical problems with structured scientific judgment and effective communication to technical and clinical stakeholders.
Comfortable working within regulated environments requiring data governance and compliance (e.g., HIPAA), supporting scalable deployment on cloud and big data platforms.