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.

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.

** 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, 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.

** 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…