Graph Data Scientist

Overview

Remote
Full Time

Skills

Tier 1
Ontologies
Semantics
Prototyping
Reasoning
Data Engineering
Storage
Real-time
Product Engineering
Research
Recruiting
Computer Science
Mathematics
Algorithms
Neo4j
Graph Databases
SPARQL
Python
Graph Theory
Modeling
Data Science
Network
Science
Finance
Reference Data
Market Analysis
Analytics
PyTorch
Machine Learning (ML)
Artificial Intelligence
Management
Managed Services
Collaboration
Partnership
Value Engineering
Effective Communication
Law

Job Details

Title: Graph Data Scientist
Location : Remote
Target Start Date : ASAP
Type: Direct Hire

About the Company
We are a global financial data and analytics software company undergoing a high-pressure, high-impact transition from legacy technology toward next-generation AI and graph-powered applications. Our organization works with tier-1 financial institutions and manages some of the world's largest and most complex datasets.
With a team of 42+ ontologists and graph data scientists already in place, we are now scaling our Graph Data Science capability to support a portfolio of advanced AI prototypes, production systems, and intelligent analytics products. We are actively hiring both Senior and Mid-Level Graph Data Scientists to contribute to this strategic transformation.

Role Overview
As a Graph Data Scientist, you will develop graph algorithms, modeling techniques, and knowledge-driven analytics to power intelligent financial applications. You will work closely with Ontologists, Data Engineers, AI researchers, and the Director of AI Initiatives to architect solutions that leverage large-scale relational datasets, graph structures, and semantic models.
This role is ideal for candidates who want to build AI/ML systems deeply rooted in graph theory, financial domain modeling, and high-impact enterprise data challenges.

Key Responsibilities
Graph Modeling & Algorithm Development
  • Build graph-based models to represent financial instruments, entities, relationships, risk exposures, and event-driven behaviors.
  • Design and implement graph algorithms for ranking, similarity, anomaly detection, community detection, influence modeling, and embeddings.
  • Apply graph neural networks (GNNs), link prediction techniques, and relational learning models to large-scale datasets.
AI & Knowledge Engineering Collaboration
  • Partner closely with Ontologists to align graph structures with ontologies, taxonomies, and semantic schemas.
  • Contribute to AI prototypes and production systems built on graph databases and graph-driven reasoning.
  • Collaborate with the AI Initiatives team to integrate graph methodologies into ML/AI pipelines.
Data Engineering & Integration
  • Work with data engineering teams to ingest, transform, and optimize large datasets for graph computation.
  • Implement scalable pipelines using graph databases (e.g., Neo4j, TigerGraph, GraphDB, Stardog).
  • Optimize graph storage, indexing, and query performance for analytics and real-time applications.
Cross-Functional Partnership
  • Engage with internal partners across product, engineering, analytics, and architecture teams.
  • Present graph insights, modeling decisions, and research findings to technical and non-technical stakeholders.
  • Inform internal hiring conversations by helping evaluate technical depth in graph/AI skillsets.


Qualifications
Required (Mid-Level, 3-7 years)
  • Master's degree in Computer Science, Mathematics, Data Science, Knowledge Engineering, AI, or a related quantitative field.
  • Hands-on experience developing graph-based models or algorithms.
  • Proficiency with graph databases (e.g., Neo4j, TigerGraph, Neptune, GraphDB) and relevant query languages (Cypher, SPARQL, Gremlin).
  • Experience with Python, ML frameworks, and data science toolkits.
  • Strong foundation in graph theory, network science, or relational modeling.
Required (Senior, 7-12+ years)
  • PhD strongly preferred in Graph Data Science, Machine Learning, Network Science, AI, Knowledge Representation, or a related discipline.
  • Demonstrated industry experience building and deploying graph-driven models at scale.
  • Ability to lead cross-functional graph/AI initiatives.
  • Experience working with large, complex, high-volume datasets.
Preferred
  • Financial domain experience: reference data, market data, risk analytics, regulatory data, ESG, or entity resolution.
  • Familiarity with GNN frameworks (PyTorch Geometric, DGL).
  • Experience integrating graph data into AI/ML and search/retrieval systems.
  • Background working in environments transitioning from legacy platforms to AI-driven architectures.

Welcome to ConsultNet, SaltClick, and Omni. As a premier national provider of technology talent and solutions, our expertise spans across project services, contract-to-hire, direct placement, and managed services, both onshore and nearshore.

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Over the last few years, thousands of consultants have found their calling with us in roles that have made a meaningful impact on their lives, enhanced their career, challenged them, and propelled them towards achieving their personal and professional goals. At ConsultNet, we believe effective communication is crucial in aligning the right job with your unique skills and professional aspirations. To us, it's all about the personal approach we take and the values we uphold.

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