Director, 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 Director-level clinical data science leader and people manager within Data & Quantitative Sciences accountable for advancing clinical data science strategy delivery excellence submission readiness and team capability across assigned studies assets or specialty areas.

Lead and develop a team of clinical data scientists and/or matrixed delivery contributors setting clear priorities enabling high-quality execution and fostering a culture of scientific rigor accountability inclusion collaboration inspection readiness and continuous improvement.

Translate complex clinical biomarker real-world external and high-dimensional data into actionable evidence that informs clinical development strategy health authority interactions regulatory submissions governance decisions and patient-focused decision making.

Shape fit-for-purpose statistical data science and advanced analytics approaches across a portfolio including exploratory analysis predictive modeling simulation integrated data review automation and AI/ML-enabled methods suitable for regulated clinical development and submission use.

Partner cross-functionally with Clinical Clinical Pharmacology PSPV Clinical Data Management Translational Sciences Regulatory Clinical Operations Statistical Programming technology/platform teams and external partners to ensure high-quality traceable analysis-ready health-authority-ready data and decision-ready insights across multiple regulatory jurisdictions.

Accountabilities:

Primary duties and responsibilities; essential functions only.

Set clinical data science direction and delivery priorities for assigned studies assets portfolio areas or capability domains ensuring alignment with development objectives functional strategy global regulatory strategy submission timelines quality expectations and stakeholder needs.

Provide people leadership for direct reports including goal setting performance management input coaching career development workload prioritization engagement and support for talent growth and retention.

Build team capability by mentoring and developing clinical data scientists creating opportunities for technical growth strengthening reproducible analytics practices and promoting effective communication of quantitative insights in study governance and regulatory contexts.

Oversee resource planning and delivery execution across assigned work balancing portfolio priorities capacity external partner contributions submission milestones and risk mitigation to ensure high-quality and timely outputs.

Lead design and interpretation of quantitative analyses using clinical trial data biomarkers real-world data external data and other relevant sources to generate evidence for study teams asset teams governance forums health authority engagements and regulatory submission packages.

Guide application of statistical machine learning simulation and visualization methods to support patient-level prediction endpoint interpretation risk assessment scenario planning integrated data review evidence generation and submission-oriented interpretation.

Define expectations for model-ready datasets and analytics-ready data flows including variable derivations data quality expectations lineage traceability metadata documentation and fit-for-purpose use in regulated clinical development inspections and submissions.

Partner with Clinical Pharmacology PSPV Translational Sciences Clinical Data Management Regulatory Statistical Programming and platform teams to ensure CDISC submission and downstream quantitative decision-making needs are reflected in study setup data review analysis planning reporting and health authority response processes.

Provide scientific technical operational and submission-readiness oversight of internal teams and external delivery partners including review of analysis plans specifications code outputs data visualization narratives documentation and interpretation of findings.

Identify communicate and mitigate risks related to data quality analytic assumptions vendor delivery resource capacity timelines reproducibility inspection readiness and regulatory acceptability of data science outputs across multiple health authorities.

Support preparation for regulatory interactions and submissions by ensuring analytical outputs are well documented traceable reproducible appropriately governed and aligned with expectations from FDA EMA PMDA NMPA MHRA and other relevant health authorities as applicable.

Drive continuous improvement in clinical data science practices through reusable code standards training automation AI-enabled workflow improvements governed data standards and adoption of industry best practices for submission-ready delivery.

Represent Clinical Data Science in cross-functional and regulatory-facing forums influencing stakeholders and ensuring quantitative insights are clearly connected to clinical development questions submission strategy health authority expectations decisions and patient impact.

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 8 years of relevant experience; or MS with 12 years of relevant experience. Equivalent combinations should be reviewed with HR.

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 functional 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 AI/ML-enabled approaches or modern data science practices in a regulated clinical development and submission environment.

Highest-priority Technical Skills

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.

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 CDISC standards and submission expectations including SDTM ADaM controlled terminology Define-XML concepts reviewer guides traceability data lineage and submission-oriented data package requirements.

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 AI/ML and advanced analytics in regulated clinical development including model development validation documentation bias/assumption assessment governance explainability and fit-for-purpose deployment in regulatory-relevant settings.

Deep knowledge of FDA EMA PMDA NMPA MHRA ICH-GCP GxP 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.

People Leadership & Behavioral Competencies

Leads with clarity accountability inclusion and enterprise mindset; creates an environment where team members can deliver grow collaborate effectively and uphold regulatory-quality expectations.

Coaches and develops direct reports and matrixed contributors providing actionable feedback supporting career growth and building future technical regulatory submission and leadership capability.

Communicates complex quantitative findings clearly to scientific operational technical executive senior leadership and health authority-facing audiences.

Influences across functions without relying solely on direct authority; builds trusted partnerships with clinical statistical programming data management regulatory technology and vendor stakeholders.

Balances scientific rigor speed quality resource capacity regulatory risk submission timelines and pragmatic delivery; proactively escalates risks with options and recommendations.

Demonstrates curiosity continuous improvement sound judgment and commitment to advancing modern clinical data science capabilities developing others and maintaining submission-ready standards.