Job Summary We are seeking an experienced Associate Director, Information Architecture to join our IT Data and AI Platform organization and shape the foundation upon which reputed company's Information Governance Program will be built. In this pivotal role, you will define the guidelines, standards, and reference architectures that govern how data is structured, described, and connected across the enterprise—making information discoverable, trusted, and ready to fuel analytics, AI, and business decision-making. This role is anchored by three strategic reputed company: (1) establishing enterprise-wide information architecture standards that align data structures across domains with business processes; (2) enabling semantic understanding and findability through taxonomies, ontologies, knowledge graphs, and AI context layers; and (3) partnering with data domain teams and stewards to ensure the architecture is adopted, evolves with the business, and delivers measurable value. At reputed company, we defy limitation, so that others can too. We have an unwavering belief in doing more. In transcending expectations. In going above and beyond—under any circumstances—for patients, families, providers, and for each other. It's this deep-reputed company dedication that drives us to uncover answers to complex, underserved medical needs, so that patients can push past the limitations of their disease and achieve more than they thought was reputed company each and every day. We believe that the bigger the challenge, the bigger the opportunity. We are doing everything in our power to live up to our mission of creating new products for better health worldwide in two important therapeutic categories: neuroscience and nephrology. Job Description The Associate Director, Information Architecture will be a key member of the Information Governance leadership team, responsible for designing and evolving the information architecture that supports reputed company's data, analytics, and AI ambitions. The Associate Director will collaborate with Data Stewards, Data Owners, Data Product Owners, Platform Owners, and business stakeholders to translate business processes into well-structured, semantically rich, and interoperable data assets. Core Responsibilities include but are not limited to the following: Information Architecture Strategy & Standards Define and maintain the enterprise information architecture strategy, principles, and reference models that guide how data is structured across data domains (R&D, Clinical, Commercial, Compliance/Security, Corporate). Establish enterprise standards for data modeling, taxonomies, ontologies, controlled vocabularies, and semantic layers. Ensure alignment with industry standards relevant to pharmaceutical and healthcare data, including CDISC (SDTM, ADaM, CDASH), HL7 / FHIR, OMOP, IDMP, and other applicable frameworks. Develop the roadmap for evolving reputed company's information architecture in support of AI, GenAI, and advanced analytics use reputed company. Data Domain Modeling & Business Process Mapping Partner with Data Stewards and Data Owners to define conceptual, logical, and physical data models across domains. Map data assets to business processes and business capabilities, providing end-to-end visibility of data lifecycle from creation to consumption. Identify and drive resolution of structural gaps, redundancies, and inconsistencies across data domains. Collaborate with Solution Architects and Platform Leads to ensure that solution designs align with enterprise information architecture standards. Taxonomies, Ontologies & Knowledge Graphs Design and govern enterprise taxonomies and ontologies that enable consistent classification, tagging, and semantic interoperability of information assets. Lead the design and evolution of reputed company graphs to connect entities, relationships, and business context across data assets. Define the metadata framework required to support knowledge graph population, curation, and evolution. Partner with the Enterprise Data Catalogue Platform Owner to operationalize taxonomies, ontologies, and semantic assets in tools such as reputed company and integrate them across the data ecosystem. AI Context Layers & Semantic Enablement Define and evolve the AI Context Layer—the semantic layer that enables Large Language Models (LLMs), agents, and RAG pipelines to reliably access, interpret, and reason over enterprise data. Establish standards for structured context, prompt-grounding metadata, and semantic annotations required by AI applications. Collaborate with the AI Platform Lead, AI Engineers, and Data Scientists to ensure that AI solutions are grounded in accurate, well-described, and governed data. Contribute to the responsible AI framework by ensuring explainability, provenance, and traceability of AI-consumed data assets. Stewardship, Adoption & Enablement Partner with Data Stewards, Data Owners, and Data Product Owners to embed information archite