Associate Director – AI/ML (R&D)

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.

Objective / Purpose:

Takeda is seeking an Associate Director to join our AI/ML & Data team in Boston, MA. This technical role focuses on implementing AI-driven drug discovery solutions across Takeda's key therapeutic areas and modalities, including small molecules and biologics. As a technical expert within our computational biology, chemistry, and data teams, you will build and deploy state-of-the-art AI/ML technologies and mathematical models to accelerate target identification, validation, and drug discovery workflows. This execution-focused role offers the opportunity to develop advanced AI platforms and implement novel approaches, such as agentic systems and reasoning models, to enhance discovery efforts across oncology, neuroscience, and inflammatory diseases.

Accountabilities:

• Build AI Solutions for Target Discovery: Develop and deploy AI/ML systems for target identification and validation in oncology, neuroscience, and GI² initiatives for small molecules and biologics. Process and analyze large-scale datasets to uncover novel therapeutic opportunities and biomarkers.

• Engineer Agentic Systems & Reasoning Models: Create and implement advanced AI systems, including agentic AI (e.g., multi-agent models, reinforcement learning) to automate hypothesis generation, experimental design, and data analysis, enabling efficient small molecule and biologic drug discovery.

• Develop AI-Integrated Tools: Build and maintain AI/ML models that integrate biological, chemical, and omics data, ensuring computational outputs provide actionable insights for drug optimization.

• Implement Machine Learning Models: Code and deploy state-of-the-art machine learning algorithms, including deep learning, graph-based models, and active learning approaches, to power in silico screening, molecule design, and biological predictions for oncology, neuroscience, and GI² drug discovery.

• Build Knowledge Graphs & Foundation Models: Develop and maintain knowledge graph technologies and foundation models (e.g., language models) that integrate diverse data sources (omics, literature), supporting scientific reasoning and hypothesis testing across drug discovery workflows.

• Execute Cross-Functional Deliverables: Collaborate with computational biology, chemistry, and digital sciences teams to implement AI solutions within experimental workflows. Ensure model outputs are production-ready and provide tangible insights across oncology, small molecule, biologics, and GI² initiatives.

• Develop AI Research Tools: Create and optimize AI-enhanced research tools for small molecule and biologic discovery. Build novel AI/ML implementations that can generate intellectual property.

• Technical Mentorship: Provide practical technical guidance to team members, demonstrating best practices in coding, model development, and AI implementation across Takeda.

• Technical Documentation & Communication: Document AI system architectures and model implementations effectively. Present technical solutions to scientific stakeholders to support decision-making across Takeda's R&D efforts.

• Educational Background: Ph.D. in Computer Science, Data Science, AI, Computational Biology, or related field preferred (or M.S. with significant relevant experience). Strong practical coding skills and proven experience building AI/ML systems for drug discovery.

• Technical AI/ML Expertise: 8+ years of experience building and deploying AI/ML or mathematical modeling solutions for drug discovery challenges. Demonstrated success implementing production-level systems independently. Direct experience coding novel AI systems (e.g., agentic systems, reasoning models) is highly advantageous.

• Proven Development Track Record: Extensive experience writing production code for machine learning systems (e.g., deep learning, reinforcement learning, graph models, active learning) in drug discovery settings.

• Applied Computational Experience: Practical experience implementing AI/ML models for small molecule and biologic drug discovery, with proven ability to create functional tools that translate computational outputs into experimental insights. Experience in oncology, neuroscience or GI² therapeutic areas is advantageous.

• Technical Stack Expertise: Advanced proficiency in Python, with experience building on cloud platforms (AWS, Azure, or GCP), and implementing solutions using machine learning frameworks (e.g., TensorFlow, PyTorch).

• Execution & Collaboration: Track record of successfully delivering AI/ML projects from concept to production within cross-functional teams. Demonstrated ability to implement working solutions that drive drug discovery programs.

• Technical Innovation & Documentation: History of developing novel AI implementations in scientific research, coupled with strong abilities to document and explain technical architectures to diverse audiences across the organization.

EDUCATION, BEHAVIOURAL COMPETENCIES AND SKILLS:

• PhD degree in a Computer Science, Data Science, AI, Computational Biology, or related field preferred with 7+ years experience , or MS with 13+ years experience, or BS with 15+ years experience 
• Strong practical coding skills and proven experience building AI/ML systems for drug discovery

• Technical AI/ML Expertise: preferably 8+ years of experience building and deploying AI/ML or mathematical modeling solutions for drug discovery challenges. Demonstrated success implementing production-level systems independently. Direct experience coding novel AI systems (e.g., agentic systems, reasoning models) is highly advantageous.

  • Proven Development Track Record: Extensive experience writing production code for machine learning systems (e.g., deep learning, reinforcement learning, graph models, active learning) in drug discovery settings. 
  • Applied Computational Experience: Practical experience implementing AI/ML models for small molecule and biologic drug discovery, with proven ability to create functional tools that translate computational outputs into experimental insights. Experience in oncology, neuroscience or GI² therapeutic areas is advantageous.
  • Technical Stack Expertise: Advanced proficiency in Python, with experience building on cloud platforms (AWS, Azure, or GCP), and implementing solutions using machine learning frameworks (e.g., TensorFlow, PyTorch).
  • Execution & Collaboration: Track record of successfully delivering AI/ML projects from concept to production within cross-functional teams. Demonstrated ability to implement working solutions that drive drug discovery programs. 
  • Technical Innovation & Documentation: History of developing novel AI implementations in scientific research, coupled with strong abilities to document and explain technical architectures to diverse audiences across the organization. 

If you are ready to be part of a forward-thinking, engineering-driven team at Takeda, contributing to transformative innovations in drug discovery through technical implementation, we encourage you to apply for this Associate Director role.