Description

Design and develop graph-based analytical solutions using Neo4j or TigerGraph to build knowledge graphs and detect complex patterns.

This role is hybrid.

Responsibilities

• Design and develop graph data models using Neo4j/TigerGraph for enterprise use cases.
• Build and manage knowledge graphs for fraud detection, relationship mapping, and pattern discovery.
• Apply data science and ML techniques to extract insights from graph data using Python and related libraries.
• Develop graph algorithms including pathfinding, centrality, clustering, and anomaly detection.
• Integrate graph platforms with Gen AI/LLM-based systems for intelligent decision-making.

Required Skills

• 12+ years of experience in data science and graph technologies.
• Strong experience in Neo4j, TigerGraph, or similar graph databases.
• Solid background in data science using Python, Pandas, Scikit-learn, and PyTorch/TensorFlow.
• Experience in graph algorithms and network analysis.
• Hands-on expertise in data modeling, ETL, and large-scale data processing.
• Understanding of Gen AI/LLM integration with structured data systems.
• Collaboration with engineering and AI teams to embed graph insights into applications.

Preferred Skills

• Experience with knowledge graphs and semantic data models.
• Exposure to hybrid architectures combining graph and AI systems.

Key Skills

Education

Not specified