Head, Data & Quantitative Sciences DD&T

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Job Description

Takeda's R&D Data, Digital & Technology (DD&T) organization is building the data and AI backbone for an R&D engine that moves faster, decides better and delivers more medicines to patients. We are looking for a DD&T leader for one of the most innovative and important partners we serve: R&D Data & Quantitative Sciences (DQS).

DQS is the quantitative engine of Takeda's pipeline. Its scientists shape portfolio decisions from candidate selection through go/no-go, design the trials that generate our evidence, produce the analyses behind every regulatory filing, and monitor the safety of our medicines before and after approval. The technology behind this work sits on the critical path for study starts, database locks, submissions and approvals, and this role owns it. As Head of DQS DD&T, you will be the senior DD&T partner to DQS leadership: setting the DD&T strategy with them, planning the portfolio of technology, data and AI work behind it, and delivering it through a global team across Cambridge and Takeda's Innovation Capability Centers. Keeping that technology reliable, validated and inspection ready is where this role starts. You will report to the Head of R&D and Global Medical DD&T and sit on that Leadership Team.

The larger opportunity is transformation. Under Takeda's AI Forward ambition, DD&T and DQS are together rebuilding how quantitative science is done: applying AI and automation across the clinical data and analysis flow, bringing simulation and predictive methods into development decisions, and building modern data foundations that let advanced quantitative and computational scientists build and deploy their own models in a governed environment. You will be as accountable for that transformation while maintaining our pipeline technology readiness and reliability. This role requires both technical fluency and creativity to move quickly on AI and data science, along with the rigor and compliance experience to know where validation, data integrity and audit readiness are non-negotiable. This is a role for someone who wants to build alongside scientists, set direction with them, and see the results show up in the pipeline.

Key Responsibilities

Strategic partnership

• Serve as the primary DD&T partner and single point of contact for the DQS Leadership Team, covering strategy, portfolio, platforms, services and support.
• Co-develop with DQS leadership a DD&T roadmap aligned to the DQS impact pillars: quantitative portfolio decisions, fit-for-purpose trial design, speed to lock, submission and access, and predictive, compliant safety risk intelligence.
• Represent DQS in DD&T planning, investment prioritization and technology governance, and represent DD&T in DQS decision forums.

Portfolio delivery and compliance

• Own the DQS technology portfolio end to end, from demand and prioritization through delivery, adoption and benefits, and ensure the technology behind development execution and regulatory filings is reliable and ready so that study timelines, database locks and submission dates are met.
• Lead the DD&T contribution to the DQS priority initiatives, embedding AI and automation into core quantitative workflows from clinical data through submission-ready outputs.
• Oversee the lifecycle of core DQS technologies, including the statistical computing environment, the analysis and reporting pipeline from clinical data to submission-ready outputs, modeling and simulation platforms, and safety and pharmacovigilance systems.
• Ensure DQS systems remain validated and compliant (e.g. GxP, 21 CFR Part 11 and EU Annex 11) where applicable, with quality by design, data integrity and always-on inspection readiness built into delivery, including model governance for AI in regulated workflows. Represent the technology position in inspections and audits.

Data, AI and Platforms

• Partner with across DD&T so that DQS builds on shared Takeda data platforms, AI infrastructure and governance, and strengthen the data foundations DQS depends upon.
• Enable DQS scientists to develop, validate and deploy their own models and AI agents, with the compute, data access and tooling they need, and keep a current view of emerging technologies and vendor roadmaps relevant to quantitative science.

People, capability and resources

• Lead, develop and retain a high-performing global team, with clear roles and ways of working across internal teams, ICC capacity and strategic suppliers.
• Manage the DQS DD&T budget, resourcing and vendor relationships, including contracting, performance and spend.

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