Senior Director, AIRx DMPK & Modeling Lead

Takeda Research is constructing a Lab of Tomorrow built on AI, automation, new ways of working, and talent with the singular vision of delivering differentiated medicines to the clinic at speed and cost. To catalyze these efforts, Takeda is creating two complementary units: AI Research Accelerator (AIRx) and Discovery Automation & Robotics (DAR). AIRx will have a group of a dedicated group of experienced biologic drug hunters with the autonomy of a biotech and the resources of a leading pharmaceutical company.

It is designed to incubate the future AI-powered operating models for large molecule discovery and deliver candidates to the clinic at industry leading speed and success rates. DMPK is a critical component of each AIRx team. As the AIRx DMPK & Modeling Lead, you will establish the quantitative and mechanistic foundation that drives portfolio decisions, integrating ADME, PK, PBPK/QSP, and AI-enabled modeling to guide programs from target hypothesis to clinical hypothesis.

You will operate within a highly externalized, AI-powered discovery model, defining how modeling is used to enable rapid, high-confidence decision making. The AIRx DMPK & Modeling Lead will combine extensive scientific and strategic expertise in DMPK and modeling with the agility to thrive in an externalized, AI-powered operating model — leveraging DMPK and modeling depth and pragmatism to direct and integrate quantitative data and decisions s will include knowledge and experience of DMPK requirements for diverse modalities (e.g., small molecules, biotherapeutics).

Responsibilities include directing CRO partners with precision and accountability to generate high-quality, interpretable data that feeds into AI-powered DMTA cycles and connecting experimental biology to translational strategy and clinical outcomes.

Portfolio-level impact across AIRx programs Quantitative Strategy & Decision Leadership:
Owns the DMPK, PK/PD, and modeling strategy across the AIRx portfolio. Ensure all programs have:
Robust human PK and dose projections, Clear exposure → target engagement → efficacy/safety hypotheses, Quantitative line of sight to clinical success. Drive model-informed decision making (MIDD) for target, modality, and candidate progression. Establish biomarker and modeling frameworks that link preclinical data to clinical outcomes. AI-Enabled Modeling & DMTA Integration:
Embed DMPK & modeling into AI-enabled DMTA cycles, ensuring rapid iteration and learning. Integrate:
In vitro / in vivo ADME data, PK, PBPK, and mechanistic (QSP) models, AI/ML-driven predictions and simulations. Ensure modeling outputs are decision-ready, interpretable, and fit-for-purpose. Design and oversee CRO-based DMPK and modeling activities with speed and rigor. Ensure:
High-quality, reproducible, and interpretable data packages, Alignment between CRO outputs and modeling need, Build scalable workflows for externalized, AI-enabled DMPK operations. Challenge scientific assumptions across the team; ensure DMPK integrity and mechanistic depth underpin all program decisions Represent DMPK perspectives at Takeda governance forums; contribute to IND-enabling packages.

Technical/Functional (Line) Expertise Strong expertise in: ADME, PK, PK/PD and application/ interpretation of mechanistic modeling in the pharmaceutical or biotech industry from early discovery to development. Leadership Demonstrated ability to operate in:
Lean, agile, high-velocity environments Externalized / CRO-driven execution models Strong cross-functional leadership across:
Chemistry, biology, safety, clinical pharmacology, and data science Ability to communicate complex quantitative insights clearly to…