AIRx, Senior Director, DMPK & Modeling Lead

Takeda•Boston, MA

10h•Hybrid

About The Position

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 at speed. This 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.

Requirements

PhD degree in a relevant scientific discipline with 12-15+ years industry experience in pharmaceutical DMPK and modeling
Proven contributions to IND filings; experience supporting IND-enabling DMPK packages
Track record designing and managing externalized CRO DMPK programs; ability to scope, oversee, and critically interpret study data
Ability to connect DMPK to clinical hypothesis and translational strategy
AI/ML data integration experience; comfortable working with large multi-source datasets
Experience in at least one therapeutic area represented across the Takeda Research portfolio
Excellent cross-functional collaboration skills; able to operate effectively in a lean, fast-paced team

Nice To Haves

Knowledge and experience of DMPK requirements for diverse modalities (e.g., small molecules, biotherapeutics).

Responsibilities

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.
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.