Back
Tier-1 brand, mid-level data scientist title, metro location, and common experience band increase qualified applicant density.
Clinical trial, RWE, and regulated biopharma experience limit cross-industry transferability.
Explicit 5+ years, clinical/RWE domain, MLOps and GenAI experience creates stringent candidate filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy predictive modeling and AI/ML solutions, including survival analysis, time series forecasting, and GenAI-powered applications, to solve clinical development challenges.
Conduct rigorous statistical analyses and exploratory data analysis on clinical trial, EHR/EMR, and real-world evidence datasets to inform clinical and operational decisions.
Collaborate with clinical, medical, and operational stakeholders to translate complex data into actionable insights, presenting findings with clear visualizations and narratives.
5+ years of progressive data science or ML engineering experience, 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 in AI/GenAI technologies such as LLMs, RAG frameworks, and prompt engineering in clinical or near-production settings.
Strong individual contributor capable of independently solving moderately complex to complex problems in clinical development analytics.
Experienced in integrating AI/GenAI technologies to augment clinical workflows, with hands-on knowledge of MLOps and cloud-based big data technologies (AWS/Azure, Spark).
Skilled at bridging technical and clinical domains, effectively communicating complex analytical concepts to cross-functional teams and aligning data-driven insights with business objectives.