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Remote Graph Theory Jobs in New York (NOW HIRING)

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Remote Graph Theory information

What is a remote graph theorist?

A Remote Graph Theorist is a mathematics professional who specializes in the study of graph theory, which explores the properties and applications of graphs—mathematical structures used to model pairwise relations between objects. Working remotely, these theorists conduct research, collaborate with other mathematicians, and solve complex problems via online platforms. Their work often supports fields like computer science, network analysis, and operations research, and can involve both theoretical exploration and practical applications.

What skills and qualifications are needed to thrive as a remote graph theorist?

To thrive as a remote Graph Theorist, you need a strong foundation in mathematics, specifically discrete mathematics and combinatorics, often backed by an advanced degree in mathematics, computer science, or a related field. Familiarity with mathematical software like SageMath, MATLAB, or Python libraries (e.g., NetworkX) and experience with collaboration tools such as GitHub and video conferencing platforms are typically required. Strong analytical thinking, problem-solving skills, self-motivation, and clear written communication are essential soft skills for effective remote work. These abilities are critical for conducting complex research, collaborating with distributed teams, and efficiently solving real-world problems from a distance.

What are common challenges faced when working remotely as a graph theorist?

One common challenge is collaborating effectively with team members, as graph theory work often involves intensive brainstorming and sharing of complex visual ideas. Remote communication tools can make it harder to whiteboard or quickly iterate on graph structures together. Additionally, accessing specialized software or computational resources may require extra coordination with IT or research teams. However, many organizations offer remote collaboration platforms and cloud-based tools to help overcome these hurdles and foster productive teamwork.

Is graph theory worth studying?

Studying graph theory is valuable for roles like remote graph theorist or related positions, as it provides foundational knowledge for analyzing networks, algorithms, and data structures. Skills in graph algorithms and mathematical reasoning are highly applicable in computer science, data analysis, and optimization fields.

What careers use remote graph theory?

Remote graph theory skills are used in careers such as data scientist, network analyst, algorithm developer, and research scientist. These roles often involve analyzing complex networks, optimizing algorithms, and working with graph-based data structures using tools like Python, R, or specialized software. Strong mathematical and programming skills are essential for success in these positions.

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

The most popular types of Graph Theory jobs in New York are:

What are popular job titles related to Remote Graph Theory jobs in New York?

For Remote Graph Theory jobs in New York, the most frequently searched job titles are:

What job categories do people searching Remote Graph Theory jobs in New York look for?

The top searched job categories for Remote Graph Theory jobs in New York are:

What cities in New York are hiring for Remote Graph Theory jobs?

Cities in New York with the most Remote Graph Theory job openings:

Remote Graph Theory jobs near you
Infographic showing various Remote Graph Theory job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 19% Part Time, 1% Temporary, and 1% Contract. Highlights an 79% Physical, 1% Hybrid, and 20% 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

Apply