Manager, AI-Ready Data Modeling

Takeda•Boston, MA

9d•$116,000 - $182,270•Hybrid

About The Position

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.

Requirements

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).
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.
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.

Nice To Haves

Demonstrated R&D domain experience preferred.
AI/ML or advanced analytics use cases (preferred).
Ontologies or semantic modeling (nice to have).

Responsibilities

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.
Apply modeling practices that enable AI/ML and advanced analytics use cases.
Ensure 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.
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.
Partner with domain experts to align models with scientific and operational needs.
Partner with data engineers and architects to ensure models are implementable.
Partner with data scientists to support analytics and AI/ML requirements.
Contribute to integration of data models into data pipelines and AI-enabled workflows.
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.
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.