Research Scientific Director, Molecule AI Development

Position: Research Scientific Director, Large Molecule AI Development
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Job Description

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 three therapeutic areas and other targeted investments, we push the boundaries of what is possible to bring life‑changing therapies to patients worldwide.

Key Responsibilities

1. AI/ML Application to Pipeline Projects

• Drive the AI/ML strategy for antibody and other large‑molecule discovery programs from target assessment through lead optimization.

• Ensure AI/ML activities are aligned with program and portfolio goals, with clear milestones, timelines, and success criteria.

• Deliver production‑grade decision tools (for example, variant ranking, develop ability risk flagging, zero‑shot design) that are seamlessly integrated into discovery workflows.

• Act as a hands‑on technical leader across multiple programs:

• Define modeling strategies and architectures

• Prioritize methods and experiments

• Review and challenge scientific output for quality and robustness

• Partner with Discovery Platform Heads and project leaders to embed AI/ML milestones into program plans, stage‑gates, and decision forums (discovery, engineering, multi‑specifics)

2. AI/ML Platform Build and Innovation

• Define and own a multi‑year platform roadmap for large‑molecule AI/ML capabilities, including models, tools, data assets, and infrastructure.

• Lead the development and deployment of foundational models for antibody and protein sequence, structure, and function prediction.

• Drive integration of physics‑based methods (for example, MD, FEP, docking) with machine learning approaches to create hybrid models with improved accuracy and generalization.

• Own data strategy for large‑molecule AI/ML (data requirement, quality standard, governance).

• Partner closely with engineering, computational, and laboratory teams to ensure the platform is usable, reliable, and scalable across programs and sites.

3. Leadership, Talent, and Culture

• Build, mentor, and retain a high‑performing, multidisciplinary team of scientists and engineers.

• Provide clear goals, expectations, and development paths and ensure high standards of scientific excellence and reproducibility.

• Champion an inclusive, collaborative, and learning‑oriented culture that values curiosity, rapid iteration, and rigorous validation.

• Communicate complex AI/ML concepts and results clearly to non‑experts, including project teams and senior leadership, enabling data‑driven decision‑making.

Qualifications

Required

• PhD degree in Computational Biology, Bioinformatics, Computer Science, or a related field with 10+ years relevant experience.

• Proven track record of leading AI‑driven projects in a research pharmaceutical setting.

• Significant depth of expertise in at least one field relevant to the job (for example, machine learning, biotherapeutic design, etc.).

• Demonstrated experience in modeling antibody/antigen sequence, structure and interaction.

• Significant depth of expertise in at least one relevant area, such as:

• Machine learning or deep learning

• Protein or biotherapeutic design

• Structural modeling or computational biophysics

• Strong analytical and problem‑solving skills, with demonstrated creativity and the ability to contribute both individually and through teams.

• Versatile communicator who can explain complex ideas to non‑specialists and influence diverse stakeholders.

Preferred

• Experience leading teams that integrate machine learning with physics‑based modeling (for example, MD, FEP, docking).

• Experience building or owning AI/ML platforms or foundational models used across multiple programs.

• Prior leadership of cross‑functional initiatives spanning discovery biology, protein engineering, and data or engineering teams.

ADDITIONAL INFORMATION

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