Minimum Requirements/Qualifications:
• PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or a related field with 8+ years of relevant experience; or a master’s degree with 12+ years of relevant experience.
• Extensive experience in clinical development within the pharmaceutical, biotechnology, or healthcare research environment, with demonstrated ability to influence cross-functional decisions at study, asset, portfolio, or function level.
• Demonstrated experience contributing to regulatory submissions, health authority interactions, inspection readiness, and submission-oriented analysis, documentation, traceability, and response activities across multiple regulatory agencies or global health authorities.
• Demonstrated experience as a people manager or formal team leader, including coaching, performance input, talent development, workload prioritization, and support for employee engagement and growth.
• Experience providing technical leadership, matrix leadership, vendor oversight, and mentorship across cross-functional, geographically distributed, or externally supported delivery models.
• Track record of advancing analytical strategy, standards, automation, artificial intelligence and machine learning-enabled approaches, or modern data science practices in a regulated clinical development and submission environment.
• Expert knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making, regulatory strategy, and submission support.
• Strong foundation in statistics and quantitative methods, including longitudinal analysis, survival methods, causal reasoning, simulation, predictive modeling, and uncertainty communication for scientific, governance, and health authority audiences.
• Experience integrating and interpreting diverse data sources, including clinical trial, biomarker, real-world, external, imaging, digital health, or other high-dimensional data as appropriate to the portfolio and regulatory context.
• Practical understanding of artificial intelligence and machine learning and advanced analytics in regulated clinical development, including model development, validation, documentation, bias and assumption assessment, governance, explainability, and fit-for-purpose use in regulatory-relevant settings.
• Hands-on fluency in R and or Python, with working knowledge of SAS and SQL; ability to guide reproducible analyses, code quality, version control, reusable workflows, validated delivery practices, and inspection-ready documentation.
• Strong working knowledge of Clinical Data Interchange Standards Consortium standards and submission expectations, including Study Data Tabulation Model, Analysis Data Model, controlled terminology, Define-XML concepts, reviewer guides, traceability, data lineage, and submission-oriented data package requirements.
• Deep knowledge of the Food and Drug Administration, European Medicines Agency, Pharmaceuticals and Medical Devices Agency, National Medical Products Administration, Medicines and Healthcare products Regulatory Agency, International Council for Harmonisation - Good Clinical Practice, Good Clinical Practice, data privacy, inspection readiness, and traceability expectations relevant to clinical data, quantitative deliverables, and global submission packages.
• Ability to establish analytical standards, technical expectations, documentation practices, quality review approaches, and submission-readiness controls that enable scalable and inspection-ready delivery across multiple health authorities.
• Communicates complex quantitative findings clearly to scientific, operational, technical, executive, senior leadership, and health authority-facing audiences.
People Leadership & Behavioral Competencies
• Influences across functions without relying solely on direct authority; builds trusted partnerships with clinical, statistical, programming, data management, regulatory, technology, and vendor teams.