Role & 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.

Preferred candidate profile

- 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.

- Valuable analytical and problem-solving skills.

- Excellent communication and stakeholder management 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.

📌 Fraud Modeling - Graph Analytics Engineer (Gurugram)
🏢 Infinites Hr Services Pune
📍 Gurugram