Associate Director - Data Modeling

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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 Development
  • Drive the assessment,& development of data models based on R&D portfolio & business needs to enable informed decision-making across R&D.
  • Provide input for the data model development life-cycle for existing data models (like Aggregated Operational Data Model) or new data models that may need to be developed.

• Leadership & Collaboration
  • Work with cross-functional teams, across R&D including the R&D DD&T Data Organization to ensure alignment and deliver impactful data solutions.

• Data Expertise:
  Strong background in data modeling, harmonization, and architecture design, with a focus on creating AI-ready data environments.
• Process Optimization:
  Ability to identify inefficiencies in data workflows and processes, designing & implementing solutions that enhance speed, accuracy, and usability.
• Advanced Analytics Enablement:
  Deep understanding of how data architecture & modelling supports AI, machine…