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Mid-level generalist ML role, strong employer brand, and metro location increase applicant density moderately.
Clinical trial and regulated-data expertise makes skills less transferable across industries.
Explicit 5+ years, clinical domain experience, and specialized ML/GenAI requirements raise filter rigidity.
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Design, develop, and deploy machine learning and AI solutions including predictive modeling and generative AI to support clinical development.
Analyze clinical datasets such as trial data, EHR/EMR, and real-world evidence to generate actionable insights and support decision-making.
Build and maintain data pipelines and collaborate with stakeholders across clinical, medical, and operations to define project objectives and communicate findings.
5+ years of experience in data science or ML engineering, preferably in biopharma, pharma, or clinical research.
Proficiency in Python, PySpark, or R with experience in clinical and statistical packages and ML frameworks.
Experience working with clinical trial data, EHR/EMR, or real-world data sources and familiarity with clinical trial data standards.
Bachelor's, Master's, or Ph.D. degree in Data Science, Statistics, Biostatistics, Computer Science, or related fields.
Demonstrated ability to independently solve moderately complex to complex problems in clinical data science with strong scientific judgment.
Experience applying advanced AI/GenAI technologies such as LLMs, RAG frameworks, and agentic AI within clinical or regulatory settings.
Strong technical skills encompassing data science, MLOps practices, and cloud/big data technologies with experience collaborating cross-functionally in regulated environments.