Research Scientific Director, Molecule AI Development

Position: Research Scientific Director, Large Molecule AI Development
Overview

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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.

We are seeking a strategic, visionary Research Scientific Director to lead the next generation of AI/ML-enabled biologics discovery s senior leadership role has two primary mandates:

Drive AI/ML application to accelerate and de-risk large-molecule pipeline projects
Build and scale AI/ML platform capabilities as a core competitive advantage for biologics discovery

You will be a key leader within the AI/ML organization, setting strategy, building partnerships across R&D, and delivering measurable impact on our biologics portfolio. You will be accountable for converting state-of-the-art AI/ML science into validated, production-grade decision tools that change how Takeda discovers, designs, and optimizes large-molecule therapeutics.

This role requires a leader who can operate at multiple altitudes, defining long-term vision and roadmaps while also ensuring scientific rigor, technical depth, and operational excellence in execution.

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, mult-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.

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