By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use.  I further attest that all information I submit in my employment application is true to the best of my knowledge.

Job Description

Job Title: Associate Director - Data Modeling

Location: Cambridge, MA

About the role:

As an Associate Director - Data Modeling, you will provide Data Modelling subject-matter-expertise to ensure high-quality analysis-ready data assets are created and utilized in a systematic manner for insight generation within R&D.

How will you contribute:

• Design, plan & implement of data modelling activities, based on R&D portfolio & business priorities.

• Design projects, activities and generate data mapping & harmonization specifications within the Data Modelling capability area.

• Provide Subject-Matter-Expertise during the implementation of data mapping and harmonization specifications.

• AI-Ready Data, Data Curation 

  • Based on R&D needs and input from R&D organizations & functions, provide input for the identification of data assets and data sets for curation and other transformations to create AI & analytics ready data sets and assets.

    • Assess, design & implement curation methodologies with input from the Data Org. capability area team members and other SMEs.

    • Accountable for the creation of data sets and assets that can be readily used for AI and advanced analytics use cases and in a consumable format for AI models.

  • Work closely with the data science and analytics teams to ensure data models support and drive advanced analytics initiatives.

• Primary and Secondary Data Architecture & Data Management

  • Design, specify and manage both primary and secondary data architecture & data management activities to ensure the efficient storage, flow and optimized use of data across the R&D organization.

• Process Development and Optimization

  • Provide input for the assessment, design and implementation of data-focused processes that lead to optimized processes and workflows across R&D to increase operational efficiency, audit & inspection readiness and other R&D goals.

    • Collaborate with R&D functions and organizations to assess & understand data & process-related gaps, pain-points etc. and provide input to create a goal state view.

    • Design and optimize the data-centric components of these processes to move processes & workflows towards the goal-state.

    • Collaborate with other process and workflow-related organizations within R&D and Takeda Enterprise organization to ensure that the goal-state processes, workflows etc. are holistic.

• • Implement best practices for data processes & operations, ensuring standardized and streamlined workflows for data & insights generation across R&D.

  • Collaborate with the other capability areas within the Data Organization to ensure that processes related to data strategy, governance, modelling and insight-generation are aligned & systematic.

• Knowledge graph development 

  • Based upon prioritized R&D needs and input from R&D organizations and functions, develop and implement knowledge graphs to support target identifications, indication expansion, pharmacology and other R&D use cases.  

  • Specify and document the knowledge models (foundational and subgraph).

  • In collaboration with Data Governance team of the Data Org. develop the governance structure to guide the onboarding and governance of data sources from internal and external sources.  

  • Provide input for the operational activities needed to synchronize the data sources and knowledge database, including observability, quality, and recency of the data, so that the knowledge graphs stay current. 

  • In collaboration with the Insight and Analytics group team of the Data & Insights Org. develop knowledge graph products with functionality related to user-interfaces, views, search, LLM-augmented RAG (Retrieval Augmented Generative) conversational interfaces and other gen-AI components etc. to enable the dissemination of the knowledge & insights across R&D.

• Data Model D