Head of Methods & AI Integration

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

Job Description

The Head of Methods & AI Integration is a senior leadership role within R&D Data and Quantitative Sciences (DQS), reporting to the Head of DQS. This role sits at the intersection of methodological innovation, AI/ML and enterprise‑scale deployment within DQS. Unlike traditional functional leadership, it is accountable for translating fragmented AI/ML and quantitative advances into standardized, regulator‑ready capabilities adopted consistently across all therapeutic areas and R&D functions. The Head of Methods & AI Integration will apply a relentless focus on scaling impact - moving innovation from pilot to enterprise deployment - and the integration of data and quantitative science depth with AI/ML and engineering fluency to build a scalable quantitative decision‑making backbone for R&D. The role demands credibility with regulators and external scientific communities alongside the operating discipline to govern reproducible, auditable, GxP‑ready methods.

About the role

Specific areas of accountability for this position include:

• Defining, integrating, and scaling advanced data & quantitative science and AI/ML methodologies into decision‑grade capabilities across R&D, embedding methodological innovation into clinical development workflows, governance, and decision‑making rather than delivering isolated pilots.
• Owning the end‑to‑end lifecycle from innovation to enterprise adoption, transforming fragmented AI and methodological advances into standardized, reusable, regulator‑ready capabilities that materially improve decision quality, speed, and development outcomes.
• Acting as the critical bridge between innovation, methods, and execution, enabling DQS to deliver a scalable quantitative decision‑making backbone across R&D.
• Positioning DQS as a global leader in AI‑enabled clinical development and decision science through internal enablement and external engagement with regulators, academia, and consortia.

How you will contribute

• Serves as a member of the DQS Leadership Team, influencing future strategy and operations with DQS and more broadly across the R&D enterprise framing the quantitative decision‑making backbone that underpins portfolio‑wide decision quality, consistency, and speed.
• Define and own the DQS methods strategy spanning data and quantitative science innovation, AI/ML, and decision science, establishing next‑generation methodologies for clinical trial design and optimization (e.g., simulation, adaptive designs) and AI‑enabled decision‑making (e.g., GenAI, causal ML, digital twins, evidence synthesis).
• Lead the systematic integration of AI/ML into clinical development workflows, shifting from pilot use to embedded, standardized capabilities delivered as reusable tools, frameworks, playbooks, and decision‑support systems.
• Own the end‑to‑end lifecycle (innovation - validation - deployment - scale), ensuring solutions are decision‑ready, reproducible, governed, and deployable in GxP/regulated environments, and eliminating "pilot‑only" efforts through repeatable scaling pathways.
• Embed advanced methods into core R&D decisions - Go/No-Go, trial design and simulation, and portfolio strategy and trade‑offs - enabling consistent, transparent, and portfolio‑comparable decision frameworks across therapeutic area units (TAUs).
• Define and implement the enterprise methods and AI governance framework, including model qualification, regulatory alignment, and standards for reproducibility, documentation, and auditability, driving standardization and reuse to reduce fragmentation and bespoke approaches across programs.
• Establish standards for model validation, method qualification, deployment readiness, and lifecycle management that are scientifically rigorous, transparent, and fit for regulatory purpose.
• Build and lead a high‑impact, multi‑disciplinary team across AI/ML methods, advanced data and quantitative science methodology, decision science, and translation/enablement, operating a hub‑and‑spoke model in partnership with SQS, QPTS, PSPV, and DD&T, etc.
• Engage regulators, academia, and consortia to shape methodological and AI standards and advance acceptance of AI‑driven approaches in regulated environments, positioning DQS as a global leader in AI‑enabled decision science.
• Drives impact on development success rates (PTRS), trial efficiency and design optimization, and reduced attrition and development timelines.
• Enhances Takeda's external influence on regulatory and scientific standards for AI‑enabled clinical development and decision science.

Preferred Qualifications

• PhD in Statistics, Data Science, or other quantitative field with ~15+ years of experience, including extensive leadership in quantitative sciences in pharma/biotech and in AI/ML or advanced analytics in regulated environments.
• MS in Statistics, Data Science, or other quantitative field with ~18+ years of equivalent experience, with a proven track record of translating innovation into enterprise‑scale capabilities and driving cross‑functional transformation across R&D.
• Deep expertise in data and quantitative science methodology and in AI/ML and modern analytic approaches, with the technical authority to set and drive functional methods strategy across R&D.
• Experience owning accountability for methodology decision‑making - selecting, qualifying, and standardizing methods that optimize the likelihood of drug R&D success.
• The ability to identify and create the technical and methodological strategic vision and implement long‑term innovation aligned with global regulatory and payer expectations, GxP environments, and trends.
• Strong command of model validation, governance, and lifecycle management, ensuring methods and AI capabilities are reproducible, auditable, and fit for regulatory purpose at scale across R&D.
• The capability to establish external networks and lead strategic DQS and R&D collaborations across industry, government, regulators, and academia to advance acceptance of AI‑driven approaches.
• Operate with an enterprise mindset, focused on scaling impact rather than isolated innovation.
• Create and develop complex, multi‑functional methods and AI strategy and mobilize organizations across R&D to adopt it.
• Bridge science, technology, and business decision‑making, and influence across global, matrixed organizations.
• Act as a strong change agent and decision maker driving AI‑enabled transformation across R&D.

Takeda Compensation and Benefits Summary

For Location: Boston, MA

U.S. Base Salary Range: $259,000.00 - $407,000.00

The estimated salary range reflects an anticipated range for this position. The actual base salary offered may depend on a variety of factors, including the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the location in which the applicant lives and/or from which they will be performing the job. The actual base salary offered will be in accordance with state or local minimum wage requirements for the job location.

EEO Statement

Takeda is proud in its commitment to creating a diverse workforce and providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, parental status, national origin, age, disability, citizenship status, genetic information or characteristics, marital status, status as a Vietnam era veteran, special disabled veteran, or other protected veteran in accordance with applicable federal, state and local laws, and any other characteristic protected by law.

Locations

Boston, MA

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time

Job Exempt

Yes

Legal Disclaimer

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.