Manager, AI-Ready Data Modeling

Manager, AI-Ready Data Modeling (RDGM DDT)

As a Manager, AI-Ready Data Modeling, you will contribute to the development of high-quality, reusable, and AI-ready data models that support data-driven decision-making across R&D. You will work across multiple projects and domains to design and deliver standardized, interoperable data structures, enabling analytics, AI/ML use cases, and scalable data integration. You will partner up closely with domain leads, data engineers, and AI teams to ensure data models are aligned to enterprise standards and support evolving R&D needs.

Data Modeling Delivery

• Design and develop conceptual, logical, and physical data models for R&D data assets.
• Translate business and scientific requirements into structured, scalable data models.
• Support modeling delivery across Research and Clinical domains in partnership with senior domain leads.

AI-Ready Data Design

• Apply modeling practices that enable:

  • AI/ML and advanced analytics use cases.

  • Consistent data inputs and context for downstream consumption.

• Structure data to improve usability, reusability, and alignment with AI-enabled workflows.

• Contribute to evolving practices for AI-ready data and standardized context generation.

Reuse and Standardization

• Contribute to development of reusable schemas, templates, and shared data models.

• Apply enterprise standards, ontologies, and naming conventions in model design.

• Promote reuse of shared data assets through data hubs, APIs, and standardized structures.

Cross-Functional Collaboration

• Partner with:

  • Domain experts to align models with scientific and operational needs.

  • Data engineers and architects to ensure models are implementable.

  • Data scientists to support analytics and AI/ML requirements.

• Contribute to integration of data models into data pipelines and AI-enabled workflows.

Data Quality and Governance Alignment

• Incorporate data quality rules, metadata, and lineage into model design.

• Ensure models align with enterprise data governance standards and support trusted, fit-for-purpose data.

What you bring to Takeda:

• Bachelor's or Master's degree in Computer Science, Data Science, Bioinformatics, or related field.

• Relevant experience in data modeling, data architecture, or data engineering.

• Experience working with structured data in complex environments (life sciences / R&D preferred).

• Demonstrated R&D domain experience preferred.

• Conceptual, logical, and physical data modeling.

• Data modeling tools and techniques.

• Data platforms (e.g., data lakes, warehouses, APIs).

• Shared data assets and schema design.

• AI/ML or advanced analytics use cases (preferred).

• Ontologies or semantic modeling (nice to have).

• Core Behavioral Competencies

• Collaboration & Teamwork Effectively partners across technical and domain teams to deliver shared outcomes.

• Execution & Delivery Focus Delivers high-quality outputs consistently across multiple projects.

• Learning Agility Quickly builds understanding across new domains and applies consistent modeling practices.

• Problem Solving Translates complex requirements into structured, practical data solutions.

• Communication Clearly articulates data concepts to both technical and non-technical stakeholders.

Key Stakeholders

• R&D Domain Leads (Research, Clinical, Operations).

• Data Engineering and Solution Architecture teams.

• Data Science and AI/ML teams.

• Data Governance and Ontology teams.

• Work on moderately complex data modeling challenges across projects.

• Balance local project requirements with enterprise standards and reuse.

• Apply judgment to adapt existing patterns to new use cases and domains.

Takeda Compensation and Benefits Summary

We understand compensation is an important factor as you consider the next step in your career. We are committed to equitable pay for all employees, and we strive to be more transparent with our pay practices.

For

Location:

Boston, MA

U.S. Base Salary Range:

$ - $

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…