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Global pharma brand, metro mid-level ML role with broad appeal increases candidate competition.
Strong biopharma and clinical data requirements limit cross-industry transferability.
Multiple mandatory filters: 5+ years, biopharma clinical data, ML/GenAI, MLOps and cloud experience.
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Design, develop, and deploy advanced ML and AI solutions including predictive models, survival analysis, and Monte Carlo simulations to address clinical development challenges.
Conduct rigorous statistical analyses on clinical trial, EHR/EMR, and real-world evidence datasets to generate actionable insights for clinical, medical, and operational teams.
Implement AI/GenAI tools such as LLMs, RAG frameworks, and agentic AI to augment clinical workflows, and maintain data pipelines ensuring computational efficiency and reliability within cloud and big data environments.
Bachelor's, Master's, or Ph.D. in Data Science, Statistics, Biostatistics, Computer Science, or related field.
5+ years progressive experience in data science or ML engineering, preferably with exposure to biopharma, pharma, or clinical research environments.
Proficiency in Python, PySpark or R; experience with clinical trial data, EHR/EMR, or RWE/RWD sources; practical working knowledge of AI/GenAI technologies including LLMs and prompt engineering.
Experience working with cloud platforms (AWS/Azure), big data technologies (Spark), and version control tools (GitHub). Work Experience Required: 5+ years
Experienced individual contributor capable of independently managing moderately complex to complex clinical data science problems in regulated biopharma settings.
Strong familiarity with clinical trial processes, data standards, and regulatory landscape, with ability to translate scientific or operational ambiguity into structured analytical solutions.
Hands-on expertise in applying AI/GenAI solutions such as LLMs, RAG, and agentic AI for clinical workflows, combined with sound scientific judgment and engineering best practices (MLOps, GitOps).