Director, Generative AI

Role Summary

Director, Generative AI at Takeda ShinrAI Center for AI/ML, based in Cambridge, MA. Lead and shape the application of machine learning and generative AI across R&D, partnering with data science teams, domain experts, and business units to identify opportunities, design solutions, and advance governance and ethics in AI/ML applications.

Responsibilities

• Partner with data science teams, domain experts, and business units to identify and prioritize opportunities to leverage machine learning, generative AI, and agentic AI to drive decision making and automation across the R&D organization
• Translate business needs into clearly scoped machine learning projects and steer solution design and implementation
• Educate, demonstrate, guide, and enable the application of machine learning and generative AI in pharmaceutical R&D operations and scientific domains
• Identify, monitor, and validate external AI/ML developments; cultivate relationships with external domain experts and partners; report and present emerging innovations to influence long-term strategy and governance
• Build relationships across the company to inform work and contribute to collaborations, including working groups and producing insightful, actionable content for decision makers
• Lead the development of common capabilities and best practices in AI/ML
• Collaborate with Ethics and Governance teams to ensure ethical AI/ML applications with broad patient and business benefits
• Support and grow AI/ML engineers and data scientists across the company
• Measure, document, and communicate the impacts of the Center’s efforts

Qualifications

• Track record of partnering cross-functionally to develop and deploy novel data solutions in production environments
• Ability to communicate complex technical topics clearly to a wide range of audiences
• Experience translating broad business ideas into micro use cases and delivering simple, effective solutions
• Experience recognizing and communicating implications of emerging technologies
• Excellent communication, prioritization, and interpersonal skills with strong attention to detail
• Advanced degree (M.S., PhD) in mathematics, applied statistics, computer science, machine learning or similar, with 8+ years of experience architecting, building, launching, and maintaining end-to-end ML systems at scale; experience with agentic and LLM-based solutions; fine-tuning large language models for domain-specific applications; transfer learning from small datasets; expertise across AI/ML problems and domains (e.g., computer vision, NLP, geometric deep learning, timeseries, reinforcement learning, multimodal learning); deep learning architectures (C/RNN, attention/memory, autoregressive) and extensions (Transformer, LSTM, Autoencoders); traditional ML models; ML Ops concepts; delivering custom software for complex R&D needs; DevOps practices for automation; diverse coding environments and software engineering workflows (testing, Git, CI/CD)
• Enthusiasm to ask questions and learn new things
• Entrepreneurial experience is desirable
• Experience in life sciences and healthcare; experience in a complex global organization is a plus

Skills

• Cross-functional collaboration and stakeholder management
• Strategic thinking and ability to translate business needs into actionable ML initiatives
• Strong analytical and problem-solving capabilities
• Effective written and verbal communication for diverse audiences
• Ethical and governance considerations in AI/ML deployments

Education

• Advanced degree (M.S., PhD) in mathematics, applied statistics, computer science, machine learning, or a related field