• PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or a related field with 3 or more years of relevant experience; or a master’s degree with 6 or more years of relevant experience. Equivalent combinations should be reviewed with Human Resources.
• Experience supporting quantitative analyses and data science activities within pharmaceutical, biotechnology, healthcare research, or other regulated clinical development environments.
• Demonstrated ability to contribute to clinical development decisions through quantitative analysis, data interpretation, and clear communication of evidence.
• Experience working on cross-functional study teams and collaborating across disciplines to achieve study objectives.
• Experience working with clinical trial data and at least one additional data type such as biomarker, real-world, external, imaging, digital health, or other high-dimensional data sources.
• Strong knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making.
• Strong foundation in statistics and quantitative methods, including longitudinal analysis, survival methods, causal reasoning, simulation, predictive modeling, and communication of uncertainty.
• Hands-on proficiency in R and or Python, with working knowledge of SAS and SQL, and the ability to support reproducible analyses, code quality, version control, and validated workflows.
• Working knowledge of Clinical Data Interchange Standards Consortium standards, including Study Data Tabulation Model, Analysis Data Model, controlled terminology, Define-XML concepts, and submission-oriented data expectations.
• Knowledge of Food and Drug Administration, European Medicines Agency, International Council for Harmonisation Good Clinical Practice, GxP, data privacy, inspection readiness, and traceability expectations relevant to clinical data and quantitative deliverables.
• Practical understanding of artificial intelligence and machine learning in regulated clinical development, including model development, validation, documentation, assumptions, bias considerations, and fit-for-purpose deployment.
• Familiarity with modern data platforms, reusable analytics workflows, automation, metadata-driven processes, and governed data standards.
• Ability to develop clear analysis specifications, visualization approaches, documentation, and interpretation summaries for scientific, operational, and study team audiences.