• PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or a related field; or a Master of Science with 3 or more years of relevant experience.
• Experience contributing to quantitative analyses and data science activities within pharmaceutical, biotechnology, healthcare research, or other regulated clinical development settings.
• 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.
• Demonstrated ability to support clinical development decisions through quantitative analysis, data interpretation, and clear communication of evidence.
• Experience working effectively on cross-functional study teams and collaborating across different disciplines to achieve study goals.
• Working knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making.
• Solid 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; ability to develop and 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.
• Ability to integrate, analyze, and interpret diverse data sources, including clinical trial, biomarker, real-world, external, imaging, digital health, or high-dimensional data as appropriate to assigned studies.
• Practical understanding of artificial intelligence and machine learning and advanced analytics in regulated clinical development, including model development, validation, documentation, assumptions, bias considerations, and fit-for-purpose deployment.
• Awareness of Food and Drug Administration, European Medicines Agency, International Council for Harmonisation Good Clinical Practice, Good Practice, data privacy, inspection readiness, and traceability expectations relevant to clinical data and quantitative deliverables.
• Ability to create clear analysis specifications, visualization approaches, documentation, and interpretation summaries suitable for scientific, operational, and study-team audiences.
• Familiarity with modern data platforms, reusable analytics workflows, automation, metadata-driven processes, and governed data standards.
• Communicates quantitative findings clearly to scientific, operational, technical, and study-team audiences.
• Builds effective working relationships across study teams and functional partners.
• Demonstrates technical credibility, sound judgment, and collaborative problem-solving skills.
• Balances scientific rigor, quality, and timely delivery while proactively communicating risks and issues.
• Demonstrates accountability for assigned deliverables and commitment to reproducible, traceable, high-quality work.
• Embraces continuous learning and adoption of innovative analytical methods, automation, and artificial intelligence-enabled approaches.