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Strong Tier-1 brand, popular Data Scientist title, and mid-level experience profile increase competition.
High due to required clinical trial, RWD/RWE, and regulated biopharma domain expertise.
Requires 5+ years, clinical/RWE expertise, MLOps and GenAI skills, so high filtering.
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Design, develop, and deploy advanced predictive models and AI/GenAI solutions for clinical trial and development challenges, including survival analysis and time series forecasting.
Conduct rigorous statistical analyses on clinical datasets such as trial data, EHR/EMR, and real-world evidence to generate actionable insights for clinical, medical, and operational stakeholders.
Build and maintain data pipelines and apply MLOps practices within cloud and big data environments while collaborating with cross-functional teams and providing technical guidance to junior data scientists.
5+ years of progressive data science or ML engineering experience with exposure to biopharma, pharma, or clinical research settings.
Strong proficiency in Python, PySpark, or R; experience with clinical statistical methodologies and modern data pipeline frameworks.
Practical experience with AI/GenAI technologies (LLMs, RAG frameworks, Agentic AI) and relevant ML frameworks, including prompt engineering and model deployment.
Bachelor's, Master's, or Ph.D. in Data Science, Statistics, Biostatistics, Computer Science, or related discipline.
Experienced applying advanced data science techniques and AI/GenAI tools specifically within clinical development or biopharma drug lifecycle contexts.
Able to independently translate complex scientific or operational problems into scalable analytical frameworks with strong scientific judgment and hypothesis-driven approaches.
Skilled in collaborating across clinical, medical, and operational functions, communicating clearly to non-technical stakeholders, and promoting best practices in MLOps and data governance in regulated environments.