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 Manager-level Clinical Data Scientist within Data & Quantitative Sciences applying statistical data science and analytical methods to support clinical development programs.

Contribute to cross-functional study teams by delivering analysis-ready data quantitative analyses visualizations and interpretation summaries for assigned studies or workstreams.

Support fit-for-purpose statistical data science and advanced analytics activities under the direction of study and functional leadership.

Collaborate with cross-functional team members to support high-quality traceable analysis-ready and submission-ready data.

Apply modern clinical data science practices including automation reusable analytics workflows and approved 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 or workstreams 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.

Translate scientific and clinical questions into analysis-ready datasets analysis specifications and reproducible analytical workflows with guidance from senior team members.

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.

Review and contribute to outputs produced by internal teams and external partners ensuring adherence to established standards processes and quality expectations.

Communicate risks related to data quality analytical assumptions timelines and quantitative outputs to study leadership and functional stakeholders.

Contribute to continuous improvement efforts through automation reusable code standard methodologies and adoption of approved technologies and workflows.

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; or MS with 3 years of relevant experience. Equivalent combinations should be reviewed with HR.

Experience contributing to quantitative analyses and data science activities within pharmaceutical biotechnology healthcare research or other regulated clinical development environments.

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 functional disciplines to achieve study objectives.

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.

Highest-priority Technical Skills

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 CDISC standards including SDTM ADaM 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 AI/ML and advanced analytics in regulated clinical development including model development validation documentation assumptions bias considerations and fit-for-purpose deployment.

Awareness of FDA EMA ICH-GCP GxP 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.

Behavioral Competencies

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 AI-enabled approaches.