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Remote Taxonomy Jobs in New York (NOW HIRING)

Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access. * Provide retrieval-ready context (RAG pipelines, vector stores, knowledge ...

Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access. * Provide retrieval-ready context (RAG pipelines, vector stores, knowledge ...

Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access. * Provide retrieval-ready context (RAG pipelines, vector stores, knowledge ...

Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access. * Provide retrieval-ready context (RAG pipelines, vector stores, knowledge ...

Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access. * Provide retrieval-ready context (RAG pipelines, vector stores, knowledge ...

Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access. * Provide retrieval-ready context (RAG pipelines, vector stores, knowledge ...

IAM/RBAC Engineer

New York, NY · Remote

$82 - $92/hr

The contractor will enforce least-privilege, govern privileged and remote access, strengthen ... Define and maintain an enterprise Azure RBAC role taxonomy and document role-to-permission mappings.

... channel taxonomy, and a backlog of content briefs and channel experiments ready to ship ... CMO * Fully remote, EST/CST hours, async-friendly * Real ownership of a defined scope with the ...

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Remote Taxonomy information

What is the difference between Remote Taxonomy vs Remote Data Analyst?

AspectRemote TaxonomyRemote Data Analyst
Required CredentialsKnowledge of taxonomy principles, often a degree in library science, information science, or related fieldsDegree in statistics, mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentPrimarily focused on organizing and classifying information, often in content management systemsAnalyzing datasets, creating reports, and deriving insights from data
Employer & Industry UsageUsed in content management, library sciences, and information architectureCommon in finance, marketing, healthcare, and tech industries

While both roles involve working with data and information, Remote Taxonomy focuses on organizing and classifying information structures, whereas Remote Data Analysts interpret and analyze data to support decision-making. Understanding these differences helps job seekers target the right roles based on their skills and career goals.

What are the most commonly searched types of Taxonomy jobs in New York?

The most popular types of Taxonomy jobs in New York are:

What cities in New York are hiring for Remote Taxonomy jobs?

Cities in New York with the most Remote Taxonomy job openings:

Infographic showing various Remote Taxonomy job openings in New York as of August 2026, with employment types broken down into 94% Full Time, 2% Part Time, and 4% Contract. Highlights an 76% Physical, 7% Hybrid, and 17% Remote job distribution.

AI-Ready Knowledge Architect

3B Staffing LLC

Manhattan, NY • On-site, Remote

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

Title : AI-Ready Knowledge Architect

Location : Remote

Duration : Contract

JOB DESCRIPTION:

We are seeking an AI-Ready Knowledge Architect to play a critical role in designing and maintaining the enterprise information architecture essential for cataloging KeyBank's data for self-service understanding and enabling AI-ready data and knowledge usage. This role defines and enforces standards for data modeling, taxonomy, semantic structures, and knowledge representation to ensure consistency, interoperability, and clarity across the organization.

The AI-Ready Knowledge Architect partners closely with business and technology teams to develop and maintain the enterprise data domain model and ontologies that support governance frameworks, trusted analytics, and downstream consumption across business intelligence (BI), applied AI/ML, and Large Language Model (LLM) use cases.

Success in this role requires the ability to translate complex theoretical concepts into scalable, governed information structures that drive adoption of the data catalog, support emerging AI capabilities, and deliver measurable value to colleagues.

ESSENTIAL JOB FUNCTIONS:

Lead the development and maintenance of the enterprise data domain model, taxonomy, and ontologies to ensure shared understanding, semantic consistency, and discoverability of data and knowledge assets.

Design and evolve information and semantic models that make enterprise data AI-ready, supporting use cases ranging from traditional analytics and BI to applied machine learning and LLM-based experiences (e.g., search, retrieval-augmented generation, and copilots).

Operationalize data models, taxonomies, and semantic structures through the Enterprise Data Catalog (Alation).

Define and enforce standards for data modeling, taxonomy, nomenclature, and semantic structures to ensure consistency and interoperability across business domains and downstream consumption patterns.

Confirm and document prioritized metadata elements for key business processes, analytical use cases, and AI-enabled workflows, ensuring alignment with governance standards and risk expectations.

Identify simplification opportunities-reduce redundancy, converge overlapping datasets, and promote canonical sources to improve trust, efficiency, and reusability across analytics and AI platforms.

Partner with analytics, data science, and AI engineering teams to ensure information architecture, metadata, and semantic context are sufficient to support explainable, governed, and trustworthy AI outcomes.

REQUIRED EXPERIENCE:

7-10 years of experience working with data, metadata, and reference data frameworks, including experience in metadata management and/or data quality monitoring

Experience leading the development of enterprise business glossaries, domain models, and ontologies to enable semantic consistency, shared understanding, and AI ready data usage.

Understanding of how semantic models, metadata, and knowledge representation enable applied AI and LLM use cases, such as search, question answering, and decision support.

Strong business acumen in relating data to business process drivers and performance management, with a value delivery mindset.

Collaborative, team focused delivery experience that drives outcomes across enterprise data, analytics, and technology organizations.

Excellent knowledge of data and metadata management principles, business analysis, and process engineering.

TECHNOLOGIES:

Knowledge Graphs

Neo4j

Stardog

Amazon Neptune / Azure Cosmos DB (Graph)

Ontology & Semantic Modeling

OWL / RDF / SKOS

Protégé

TopBraid

Stardog Studio

Enterprise Data & Knowledge Catalogs

Alation

Collibra

Microsoft Purview

DataHub

Knowledge Modeling Techniques

Ontologies & domain models

Business vocabularies & taxonomies

Semantic normalization

Entity & relationship modeling

AI Context Delivery (Grounding Layer) Vector databases (Pinecone, Weaviate, Azure AI Search)

Graph + vector retrieval (hybrid RAG)

Metadata-driven prompt context