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Job Description
About the role:
At Takeda, we are a forward-looking, world-class R&D organization that unlocks innovation and delivers transformative therapies to patients. By focusing R&D efforts on four therapeutic areas and other targeted investments, we push the boundaries of what is possible in order to bring life-changing therapies to patients worldwide.
As an Associate Director-level clinical data science leader within Data & Quantitative Sciences, you will translate complex clinical, biomarker, and external data into actionable evidence that informs clinical development decisions.
How you will contribute:
Lead fit-for-purpose statistical, data science, and advanced analytics approaches across assigned studies, assets, or specialty areas, including exploratory analysis, predictive modeling, simulation, and integrated data review.
Partner cross-functionally with Clinical, Clinical Pharmacology, PSPV, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, and external partners to ensure high-quality, traceable, analysis and submission-ready data and decision-ready insights.
Advance modern ways of working by applying AI/ML, automation, reusable analytics workflows, and governed data standards while maintaining scientific rigor, regulatory awareness, and patient-focused decision making.
Lead clinical data science strategy and delivery for one or more studies, assets, or capability areas, ensuring alignment with development objectives, timelines, quality expectations, and stakeholder needs.
Design and/or execute quantitative analyses using clinical trial data, biomarkers, real-world data, external data, and other relevant sources to generate interpretable insights for study teams and governance forums.
Apply appropriate statistical, machine learning, simulation, and visualization methods to support patient-level prediction, endpoint interpretation, risk assessment, scenario planning, and evidence generation.
Define requirements for model-ready datasets and analytics-ready data flows, including variable derivations, data quality expectations, lineage, traceability, metadata, and documentation sufficient for regulated clinical development use.
Partner with Clinical Pharmacology PSPV, Translational Sciences, Clinical Data Management, Regulatory, and platform teams to ensure that CDISC, submission, and downstream quantitative decision-making needs are built into study setup, data review, and reporting processes.
Provide scientific and technical oversight of internal and external delivery partners, including review of analysis plans, specifications, code, outputs, data visualization, and interpretation of findings.
Identify, communicate, and mitigate risks related to data quality, analytic assumptions, vendor delivery, timelines, reproducibility, and regulatory acceptability of data science outputs.
Drive continuous improvement in clinical data science practices through reusable code, standards, training, mentoring, automation, AI-enabled workflow improvements, and adoption of industry best practices.
Mentor junior colleagues or delivery partners in clinical data science methods, reproducible analytic practices, technical problem solving, and effective communication of quantitative insights.
Minimum Requirements/Qualifications:
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 8+ years of relevant experience. Equivalent combinations should be reviewed with HR.
Significant experience in clinical development within the…