Fraud Modeling – Graph Analytics Engineer

Fraud Modeling – Graph Analytics Engineer

Experience: 3–10 Years

Location: Bengaluru / Gurugram

Notice Period: Immediate to 60 Days

Job Summary

We are seeking an experienced Fraud Modeling Engineer with expertise in graph databases and graph-based fraud detection solutions. The ideal candidate will have hands-on experience with graph technologies, graph query languages, and Graph Neural Networks (GNNs) to build scalable fraud detection and risk analytics models.

Key Responsibilities

• Design and develop graph-based fraud detection models using graph databases.
• Build and optimize graph data models for fraud analytics and risk detection.
• Develop and execute graph queries using Cypher and Gremlin.
• Work with AWS Neptune and/or Neo4j for graph database implementation.
• Build and deploy Graph Neural Network (GNN) models for fraud detection and link analysis.
• Analyze complex relationships and patterns to identify fraudulent activities.
• Collaborate with data scientists, engineers, and business stakeholders to deliver fraud analytics solutions.
• Optimize graph database performance and ensure scalability of fraud detection systems.

Required Skills

• Strong experience in Fraud Modeling and Fraud Analytics.
• Hands-on experience with AWS Neptune and/or Neo4j.
• Proficiency in Cypher Query Language and Gremlin.
• Experience with Graph Neural Networks (GNNs).
• Strong understanding of graph data structures, relationship modeling, and network analytics.
• Good analytical and problem-solving skills.
• Excellent communication and stakeholder management skills.

Preferred Skills

• Experience in Financial Services, Banking, or FinTech fraud detection.
• Knowledge of Machine Learning and AI techniques for fraud prevention.
• Experience working with cloud platforms, preferably AWS.
• Familiarity with Python or Java for graph analytics is an added advantage.

Mandatory Skills

• Fraud Modeling
• AWS Neptune
• Neo4j
• Cypher Query
• Gremlin
• Graph Neural Networks (GNN)