Education & Competencies (Technical and Behavioral):
Essential and desirable education and competency requirements to perform the primary responsibilities of the job.
• PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or related field with 5+ years of relevant experience; or MS with 7+ years of relevant experience. Equivalent combinations should be reviewed with HR.
• 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 effective communication of evidence.
• Experience working effectively on cross-functional study teams and collaborating across functional disciplines to achieve study objectives.
• Experience working with clinical trial data and one or more additional data types such as biomarker, real-world, external, imaging, digital health, or other high-dimensional data sources.
Highest-priority Technical Skills
• 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; ability to develop, review, and support reproducible analyses, code quality, version control, and validated workflows.
• Working knowledge of CDISC standards, including SDTM, ADaM, controlled terminology, Define-XML concepts, and submission-oriented data expectations.
• Experience integrating, analyzing, and interpreting 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 AI/ML and advanced analytics in regulated clinical development, including model development, validation, documentation, assumptions, bias considerations, and fit-for-purpose deployment.
• Knowledge of FDA, EMA, ICH-GCP, GxP, data privacy, inspection readiness, and traceability expectations relevant to clinical data and quantitative deliverables.
• Ability to develop 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 leadership audiences.
• Builds effective working relationships across study teams and functional partners.
• Demonstrates strong 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 AI-enabled approaches.