Head of AI Methods & Enterprise Decision Science

Takeda Pharmaceutical Company Limited; Boston (MA); On-site; USD 259,000 - 407,000; Full time

Job summary

Takeda Pharmaceutical Company Limited in Boston, MA seeks a senior leader to head Methods & AI Integration within DQS. This role scales AI/ML and quantitative methods from pilots to enterprise, governing standards, governance, and regulatory readiness across R&D.

You will own the lifecycle from innovation to adoption, ensuring decision-ready, auditable, and scalable capabilities in a regulatory setting, interfacing with regulators and external scientific communities.

Qualifications

• PhD in Statistics, Data Science, or other quantitative field with ~15+ years of experience in pharma/biotech.
• MS in Statistics, Data Science, or other quantitative field with ~18+ years equivalent experience, proven enterprise-scale impact.
• Deep expertise in data and quantitative science methodology and AI/ML in regulated environments.
• Experience owning methodology decision-making and standardizing methods for regulatory purposes.

Responsibilities

• Define and own the DQS methods strategy spanning data science, AI/ML, and decision science.
• Lead integration of AI/ML into clinical development workflows, delivering reusable tools and playbooks.
• Own end-to-end lifecycle from innovation to enterprise adoption, ensuring regulator-ready capabilities.
• Engage regulators, academia, and consortia to shape AI standards and adoption.
• Build and lead a multi-disciplinary team across AI/ML, data science, and translation.
• Drive impact on trial design, Go/No-Go decisions, and portfolio strategy.

Skills

Leadership

AI/ML expertise

Regulatory alignment

Strategic planning

Cross-functional collaboration

Change management

Education

PhD in Statistics or Data Science

MS in Statistics or Data Science

Job description

Takeda Pharmaceutical Company Limited in Boston, MA seeks a senior leader to head Methods & AI Integration within DQS. This role scales AI/ML and quantitative methods from pilots to enterprise, governing standards, governance, and regulatory readiness across R&D.

You will own the lifecycle from innovation to adoption, ensuring decision-ready, auditable, and scalable capabilities in a regulatory setting, interfacing with regulators and external scientific communities.