Why This Role Stands Out
This leadership role at Takeda Pharmaceutical offers a unique opportunity to shape the future of AI integration in drug discovery, impacting patient outcomes globally. You will thrive here if you are a strategic thinker with expertise in advanced analytics and a passion for building and mentoring high-performing teams within a collaborative, hybrid work environment. Take the next step in your career and apply to lead groundbreaking innovation.
Quick Overview
Job Description
Takeda Pharmaceutical seeks a Head of Methods & AI Integration to lead development and deployment of advanced analytical, statistical, and AI/ML methods across our R&D portfolio. You will define the strategy for AI integration in drug discovery, clinical development, and real-world evidence, partnering with biostatistics, data science, and therapeutic area leaders. Responsibilities include building and mentoring a high-performing methods team, setting modeling and data standards, evaluating emerging technologies, and ensuring compliant, ethical use of AI to accelerate innovation and improve patient outcomes worldwide.
Responsibilities
- Define and lead AI and advanced methods strategy across R&D functions
- Oversee development, validation, and deployment of statistical and AI/ML models
- Partner with biostatistics, clinical, and data science teams to design data-driven studies
- Establish modeling, data quality, and governance standards for AI use
- Build, mentor, and manage a high-performing quantitative and AI methods team
- Evaluate and implement emerging AI/ML and analytics technologies
- Ensure ethical, regulatory-compliant use of AI in drug development
- Collaborate with global stakeholders to align methods with portfolio priorities
- Communicate complex analytical insights to scientific and executive leaders
- Drive continuous improvement in analytics capabilities to accelerate patient impact
Required Skills
- Machine learning and deep learning
- Biostatistics and experimental design
- Bayesian and advanced statistical modeling
- Real-world data and real-world evidence analytics
- Cloud data platforms (e.g., AWS, Azure, GCP)
- Programming in Python and/or RMLOps and model lifecycle management
- Data governance and regulatory compliance for AILeadership of technical and scientific teams
- Scientific communication to non-technical stakeholders
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