Senior Manager, Clinical Data Scientist

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Objective / Purpose:

Describe at the highest level the team where this job sits and how this role will contribute to the teams 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.