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The Head of Methods & AI Integration is a senior leadership role within R&D Data and Quantitative Sciences (DQS), reporting to the Head of DQS. This role sits at the intersection of methodological innovation, AI/ML and enterprise-scale deployment within DQS. Unlike traditional functional leadership, it is accountable for translating fragmented AI/ML and quantitative advances into standardized, regulator-ready capabilities adopted consistently across all therapeutic areas and R&D functions. 

The Head of Methods & AI Integration will apply a relentless focus on scaling impact — moving innovation from pilot to enterprise deployment — and the integration of data and quantitative science depth with AI/ML and engineering fluency to build a scalable quantitative decision-making backbone for R&D. The role demands credibility with regulators and external scientific communities alongside the operating discipline to govern reproducible, auditable, GxP-ready methods. 

Specific areas of accountability for this position include:   

• Defining, integrating, and scaling advanced data & quantitative science and AI/ML methodologies into decision-grade capabilities across R&D, embedding methodological innovation into clinical development workflows, governance, and decision-making rather than delivering isolated pilots. 

• Owning the end-to-end lifecycle from innovation to enterprise adoption, transforming fragmented AI and methodological advances into standardized, reusable, regulator-ready capabilities that materially improve decision quality, speed, and development outcomes. 

• Acting as the critical bridge between innovation, methods, and execution, enabling DQS to deliver a scalable quantitative decision-making backbone across R&D. 

• Positioning DQS as a global leader in AI-enabled clinical development and decision science through internal enablement and external engagement with regulators, academia, and consortia. 

• Serves as a member of the DQS Leadership Team, influencing future strategy and operations with DQS and more broadly across the R&D enterprise R&D framing the quantitative decision-making backbone that underpins portfolio-wide decision quality, consistency, and speed.  

• Define and own the DQS methods strategy spanning data and quantitative science innovation, AI/ML, and decision science, establishing next-generation methodologies for clinical trial design and optimization (e.g., simulation, adaptive designs) and AI-enabled decision-making (e.g., GenAI, causal ML, digital twins, evidence synthesis). 

• Lead the systematic integration of AI/ML into clinical development workflows, shifting from pilot use to embedded, standardized capabilities delivered as reusable tools, frameworks, playbooks, and decision-support systems. 

• Own the end-to-end lifecycle (innovation → validation → deployment → scale), ensuring solutions are decision-ready, reproducible, governed, and deployable in GxP/regulated environments, and eliminating “pilot-only” efforts through repeatable scaling pathways. 

• Embed advanced methods into core R&D decisions — Go/No-Go, trial design and simulation, and portfolio strategy and trade-offs — enabling consistent, transparent, and portfolio-comparable decision frameworks across therapeutic area units (TAUs). 

• Define and implement the enterprise methods and AI governance framework, including model qualification, regulatory alignment, and standards for reproducibility, documentation, and auditability, driving standardization and reuse to reduce fragmentation and bespoke approaches across programs. 

• Establish standards for model validation, method qualification, deployment readiness, and lifecycle management that are scientifically rigorous, transparent, and fit for regulatory purpose. 

• Build and lead a high-impact, multi-disciplinary team across AI/ML methods, advanced data and quantitative science methodology, decision science, and translation/enablement, operating a hub-and-spoke model in partnership with SQS, QPTS, PSPV, and DD&T, etc. 

• Engage regulators, academia, and consortia to shape methodological and AI standards and advance acceptance of AI-driven approaches in regulated environments, positioning DQS as a global leader in AI-enabled decision science. 

• Drives impact on development success rates (PTRS), trial efficiency and design optimization, and reduced attrition and development timelines. 

• Enhances Takeda’s external influence on regulatory and scientific standards for AI-enabled clinical development and decision science. 

• PhD in Statistics, Data Science, or other quantitative field with ~15+ years of experience, including extensive leadership in quantitative sciences in pharma/biotech and in AI/ML or advanced analytics in regulated environments. 

• MS in Statistics, Data Science, or other quantitative field with ~18+ years of equivalent experience, with a proven track record of translating innovation into enterprise-scale capabilities and driving cross-functional transformation across R&D. 

• Deep expertise in data and quantitative science methodology and in AI/ML and modern analytic approaches, with the technical authority to set and drive functional methods strategy across R&D. 

• Experience owning accountability for methodology decision-making — selecting, qualifying, and standardizing methods that optimize the likelihood of drug R&D success. 

• The ability to identify and create the technical and methodological strategic vision and implement long-term innovation aligned with global regulatory and payer expectations, GxP environments, and trends. 

• Strong command of model validation, governance, and lifecycle management, ensuring methods and AI capabilities are reproducible, auditable, and fit for regulatory purpose at scale across R&D. 

• The capability to establish external networks and lead strategic DQS and R&D collaborations across industry, government, regulators, and academia to advance acceptance of AI-driven approaches. 

• Operate with an enterprise mindset, focused on scaling impact rather than isolated innovation.

• Create and develop complex, multi-functional methods and AI strategy and mobilize organizations across R&D to adopt it. 

• Bridge science, technology, and business decision-making, and influence across global, matrixed organizations. 

• Act as a strong change agent and decision maker driving AI-enabled transformation across R&D.