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Job Opportunity: Head of Computational Science & Data Strategy at Takeda

Job Description:

Objective / Purpose:

The Head of Computational Science & Data Strategy is a key member of Takeda’s Research Leadership Team (RLT). This role is responsible for designing the 'ONE Research' data strategy, with input from the RLT and cross-functional partners such as the Data Sciences Institute (DSI) and Pharmaceutical Sciences (Pharm Sci).

This leader will oversee a team of computational scientists, driving the implementation of cutting-edge computational approaches including Artificial Intelligence (AI) and Machine Learning (ML). Key areas of focus include target identification, pathway analysis, biomarker discovery, and optimization of both small and large molecules.

Collaborating with Takeda’s Center for External Innovation (CEI), this leader ensures Takeda stays at the forefront of emerging technologies and maintains strong partnerships across academia, medical centers, and biotech.

As Takeda’s Research data becomes increasingly digitized, this leader ensures data integrity and the seamless integration of advanced analytics from discovery to clinical data, providing real-time insights for decision-making and regulatory submissions.

With a bridging seat on the DSI leadership team, the Head of Computational Science & Data Strategy ensures alignment with DSI’s data infrastructure and governance responsibilities. This collaboration fosters Research and Development projects that leverage DSI’s strengths in quantitative pharmacology, biostatistics, and big data.

Under Pharm Sci sponsorship, this role guides computational science applications relevant to drug manufacturing, including retrosynthetic AI and computational energetics. The role spans Research and Pharm Sci, promoting a cross-functional, collaborative strategy rooted in research.

In summary, the Head of Computational Science & Data Strategy scales computational science enterprise-wide, enabling Takeda Research to harness AI, ML, and digital capabilities, thus improving the speed and quality of our medicines.

Key Accountabilities:

Strategic Vision and Implementation:

• Articulate and implement a vision for applying computational science across R&D and Pharm Sci.
• Drive innovation by integrating AI, ML, and computational approaches across R&D projects.
• Develop strategies to maximize data science applications’ value in AI/ML, Computational Chemistry, and Biology.

Enterprise Leadership and Team Building:

• Build and inspire a world-class team of data science experts.
• Provide strategic, scientific, and operational leadership to foster a collaborative and excellent data sciences team.

Data Governance and Standards:

• Establish and enforce robust data governance and standards across R&D.
• Ensure data democratization, making data structured, accessible, searchable, and usable.
• Collaborate with stakeholders to maintain a state-of-the-art data infrastructure.

Portfolio and Resource Management:

• Conduct regular portfolio reviews to assess progress and optimize resource allocation.
• Prioritize data science initiatives in line with overall R&D strategy and portfolio priorities.
• Allocate resources to support and advance R&D data science initiatives effectively.

Performance Measurement and Reporting:

• Develop and track key performance indicators (KPIs) to measure data science initiatives’ impact.
• Ensure integration of scientific, portfolio, and business information into critical analysis and reporting.
• Provide regular updates to senior leadership on progress, milestones, and strategic recommendations.
• Ensure data integrity and accuracy in all reporting and communication.

Stakeholder Engagement and Representation:

• Engage with internal and external stakeholders as the primary liaison for R&D data science.
• Raise the visibility of R&D data science within the company and the broader scientific community.
• Continuously review data and technology strategies to improve effectiveness.

Operational Efficiency and Process Improvement: