- 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
- Solid 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.
- 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.