Associate Director - Data Modeling

Takeda•Cambridge, MA

6d•$154,400 - $242,550•Onsite

About The Position

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.

Requirements

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 learning, and analytics, ensuring seamless integration and data flow.
Strategic Leadership: Ability to align data modeling and process optimization with broader business objectives, providing guidance and vision to the data team.
Cross-functional Collaboration: Lead efforts across R&D functions & organizations, to drive alignment between business, and data teams to ensure success in implementing data strategies.
Proven experience in data modeling across multiple R&D domains, data architecture (relational DBs, hierarchical DBs etc.) principles, data mapping & harmonization.
Strong knowledge of AI-ready data processes, including data preparation, cleaning, curation, transformation and optimization for machine learning and AI applications.
Good knowledge of data governance principles, quality, and compliance, ensuring that data models and processes align with regulated industry standards.
Minimum: Bachelor’s degree in Computer Science, Data Science, Engineering, Information Technology, or a related field.
Experience in data modeling, data architecture, and process optimization, with a focus on large-scale, complex data environments.
5-8 years of experience.
Experience in AI-ready data preparation, data modeling for machine learning, and harmonizing data from multiple sources.
Proven track record of leading data architecture and modeling initiatives, including the successful implementation of data processes and systems.
Deep expertise in data modeling and data architecture.
Strong background in AI and machine learning data preparation, including cleaning, transformation, and optimization.
Good understanding of data governance, data management, and insight-generation principles.
Working knowledge of cloud technologies (AWS, Azure, Google Cloud) and data platforms (data lakes, data warehouses).
Excellent communication, presentation, and leadership skills, with the ability to engage stakeholders at all levels of the organization.

Nice To Haves

Preferred: Master’s degree in Data Science, Information Management, or a related field.
Experience in the pharmaceutical, healthcare, or life sciences industries.
Familiarity with data visualization tools (e.g., Tableau, Power BI) and data processing frameworks (e.g., Spark, Hadoop).
Knowledge of regulatory data standards and experience with data privacy and compliance requirements.

Responsibilities

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