Director, Clinical Data Scientist

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Job Description

About the role:

You will serve as a Director-level clinical data science leader and people manager within Data and Quantitative Sciences. In this role, you will help shape clinical data science strategy, support high-quality delivery, strengthen submission readiness, and build team capability across assigned studies, assets, or specialty areas.

You will lead and develop a team of clinical data scientists and matrixed contributors, set clear priorities, and foster a culture of scientific rigor, accountability, inclusion, collaboration, inspection readiness, and continuous improvement. You will turn complex clinical, biomarker, real-world, external, and high-dimensional data into evidence that supports clinical development strategy, health authority interactions, regulatory submissions, governance decisions, and patient-focused decision making.

You will shape statistical, data science, and advanced analytics approaches across a portfolio, including exploratory analysis, predictive modeling, simulation, integrated data review, automation, and artificial intelligence and machine learning methods that are suitable for regulated clinical development and submission use.

You will partner with colleagues across Clinical, Clinical Pharmacology, pharmacovigilance, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, Statistical Programming, technology and platform teams, and external partners to ensure high-quality, traceable, analysis-ready, health-authority-ready data and decision-ready insights across multiple regulatory jurisdictions.

How you will contribute:

• Set clinical data science direction and delivery priorities for assigned studies, assets, portfolio areas, or capability domains, aligning work 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 clear communication of quantitative insights in study, governance, and regulatory settings.

• 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.

• 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, health authority interactions, and regulatory submission packages.

• Guide the use 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, pharmacovigilance, Translational Sciences, Clinical Data Management, Regulatory, Statistical Programming, and platform teams to ensure data standards, submission needs, 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 visualizations, 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 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.

• Drive continuous improvement in clinical data science practices through reusable code, standards, training, automation, artificial intelligence-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, helping connect quantitative insights to clinical development questions, submission strategy, health authority expectations, decisions, and patient impact.

MinimumRequirements/Qualifications: 

• Doctor of Philosophy in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or a related field with 8 or more years of relevant experience; or a Master of Science degree with 12 or more years of relevant experience. Equivalent combinations should be reviewed with Human Resources.

• 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, 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.

• Hands-on fluency in R and or Python, with working knowledge of SAS and Structured Query Language; 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.

• 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 deployment in regulatory-relevant settings.

• Deep knowledge of 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 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.

• 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.

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 colleagues.

• 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.

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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