Research Scientific Director, Large Molecule AI Development

Role Summary

Takeda's AI/ML organization seeks a strategic, visionary Research Scientific Director to lead the next generation of AI/ML-enabled biologics discovery. The role combines scientific leadership and platform-building to accelerate large-molecule discovery and to deliver production-grade AI tools integrated into discovery workflows. The position requires defining long-term vision and roadmaps while ensuring scientific rigor, technical depth, and operational excellence across programs and sites.

Responsibilities

• 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, developability 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)
• 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 (e.g., 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
• 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

Skills

• Strategic AI/ML leadership in biotherapeutics and large-molecule discovery
• Cross-functional collaboration across biology, engineering, data science, and operations
• Ability to translate complex AI concepts into practical, production-grade tools
• Strong communication of results to non-experts and senior leadership

Education

• PhD degree in Computational Biology, Bioinformatics, Computer Science, or related field