Director, Clinical Data Scientist - Statistics

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Job Description

About the role:

You will lead a Director-level clinical data science team within Data and Quantitative Sciences, guiding strategy, delivery, submission readiness, and team capability across assigned studies, assets, or specialty areas. This role brings together clinical trial data, biomarkers, real-world data, external data, and other complex data sources to support clinical development decisions, regulatory interactions, and submission packages. You will work across Clinical, Clinical Pharmacology, Pharmacovigilance and Patient Safety, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, Statistical Programming, technology teams, and external partners to deliver high-quality, traceable, analysis-ready data and clear quantitative insights for global regulatory use.

How you will contribute:

• Lead the 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, regulatory interactions, and submission packages.
• Guide the use of statistical methods, machine learning, simulation, and visualization to support patient-level prediction, endpoint interpretation, risk assessment, scenario planning, integrated data review, evidence generation, and submission-focused interpretation.
• Set clinical data science direction and delivery priorities for assigned studies, assets, portfolio areas, or capability domains, aligned with development objectives, functional strategy, global regulatory strategy, submission timelines, quality expectations, and team needs.
• 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 findings.
• Identify, communicate, and reduce 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.
• Oversee resource planning and delivery execution across assigned work, balancing portfolio priorities, capacity, external partner contributions, submission milestones, and risk mitigation to support high-quality and timely outputs.
• Partner with Clinical Pharmacology, Pharmacovigilance and Patient Safety, Translational Sciences, Clinical Data Management, Regulatory, Statistical Programming, and platform teams to ensure CDISC, submission, and downstream quantitative decision needs are reflected in study setup, data review, analysis planning, reporting, and health authority response processes.
• 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.
• Support preparation for regulatory interactions and submissions by ensuring analytical outputs are well documented, traceable, reproducible, appropriately governed, and aligned with expectations from the Food and Drug Administration, European Medicines Agency, Pharmaceuticals and Medical Devices Agency, National Medical Products Administration, Medicines and Healthcare products Regulatory Agency, and other relevant health authorities, as applicable.
• 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 clear communication of quantitative insights in study, governance, and regulatory settings.
• Represent Clinical Data Science in cross-functional and regulatory-facing forums, helping connect quantitative insights to clinical development questions, submission strategy, health authority expectations, decisions, and patient impact.
• Drive continuous improvement in clinical data science practices through reusable code, standards, training, automation, artificial intelligence and machine learning-enabled workflow improvements, governed data standards, and adoption of industry best practices for submission-ready delivery.

Minimum Requirements/Qualifications:

• PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or a related field with 8+ years of relevant experience; or a master’s degree with 12+ years of relevant experience.
• 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 function 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, artificial intelligence and machine learning-enabled approaches, or modern data science practices in a regulated clinical development and submission environment.
• 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.
• 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 artificial intelligence and machine learning and advanced analytics in regulated clinical development, including model development, validation, documentation, bias and assumption assessment, governance, explainability, and fit-for-purpose use in regulatory-relevant settings.
• 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 Clinical Data Interchange Standards Consortium standards and submission expectations, including Study Data Tabulation Model, Analysis Data Model, controlled terminology, Define-XML concepts, reviewer guides, traceability, data lineage, and submission-oriented data package requirements.
• Deep knowledge of the Food and Drug Administration, European Medicines Agency, Pharmaceuticals and Medical Devices Agency, National Medical Products Administration, Medicines and Healthcare products Regulatory Agency, International Council for Harmonisation - Good Clinical Practice, Good Clinical Practice, 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.
• Communicates complex quantitative findings clearly to scientific, operational, technical, executive, senior leadership, and health authority-facing audiences.

People Leadership & Behavioral Competencies

• Influences across functions without relying solely on direct authority; builds trusted partnerships with clinical, statistical, programming, data management, regulatory, technology, and vendor teams.
• Balances scientific rigor, speed, quality, resource capacity, regulatory risk, submission timelines, and practical 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.
• Leads with clarity, accountability, inclusion, and an enterprise mindset; creates an environment where team members can deliver, grow, collaborate, 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.

More about us:

At Takeda, we are transforming patient care through the development of novel specialty pharmaceuticals and best in class patient support programs. Takeda is a patient-focused company that will inspire and empower you to grow through life-changing work.

Certified as a Global Top Employer, Takeda offers stimulating careers, encourages innovation, and strives for excellence in everything we do. We foster an inclusive, collaborative workplace, in which our teams are united by an unwavering commitment to deliver Better Health and a Brighter Future to people around the world.

This position is currently classified as "hybrid" following Takeda's Hybrid and Remote Work policy.

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