Senior Manager, Clinical Data Scientist

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Job Description

Objective / Purpose:

Describe at the highest level the team where this job sits and how this role will contribute to the team’s delivery of critical function.

• Serve as a Senior Manager-level Clinical Data Scientist within Data & Quantitative Sciences, applying statistical, data science, and analytical methods to support clinical development programs.
• Partner with cross-functional study teams to deliver analysis-ready data, perform quantitative analyses, interpret results, and generate decision-support insights.
• Deliver fit-for-purpose statistical, data science, and advanced analytics activities for assigned studies and study-level workstreams.
• Collaborate with Clinical, Clinical Pharmacology, PSPV, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, Statistical Programming, and external partners to support high-quality, traceable, analysis-ready, and submission-ready data.
• Apply modern clinical data science practices, including automation, reusable analytics workflows, and AI/ML-enabled approaches, while maintaining scientific rigor, regulatory awareness, and patient-focused decision making.

Accountabilities:

Primary duties and responsibilities; essential functions only.

• Execute clinical data science activities for assigned studies, ensuring timely delivery of high-quality analyses, data review, and quantitative insights that support study objectives.
• Perform exploratory analyses, data visualization, and quantitative assessments using clinical trial, biomarker, external, and real-world data sources.
• Collaborate with Clinical Data Management, Clinical Pharmacology, PSPV, Clinical Operations, and Translational Sciences to support study objectives and evidence generation.
• Translate scientific and clinical questions into analysis-ready datasets, specifications, and reproducible analytical workflows.
• Support integrated data review activities by identifying data trends, inconsistencies, and potential risks requiring further investigation.
• Apply established statistical, machine learning, simulation, and visualization methods to support interpretation of study results and development decisions.
• Contribute to the review of analysis outputs, visualizations, and technical documentation to ensure quality, traceability, and reproducibility of deliverables.
• Review and contribute to analysis outputs produced by internal teams and external partners, ensuring quality and adherence to established standards and processes.
• Identify and communicate risks related to data quality, analytical assumptions, timelines, and quantitative outputs to functional stakeholders.
• Contribute to continuous improvement efforts through automation, reusable code, standard methodologies, and adoption of approved technologies and workflows.
• Contribute to departmental standards, process improvements, and technology adoption initiatives as assigned.
• Share technical expertise and support onboarding and development of less experienced team member

Education & Competencies (Technical and Behavioral):

Essential and desirable education and competency requirements to perform the primary responsibilities of the job.

Education / Experience

• 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.

Behavioral Competencies

• 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.

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