Role will operate primarily in feature engineering support for the Fraud AI/ML modeling team, with a focus on feature development and graph-based data assets. Personnel will engineer, maintain, and optimize features within the AWS Feature Store using PySpark; create and enhance graph relationships, node structures, and embeddings within Neo4j; support data quality monitoring and feature governance; and partner with modelers to enable scalable, production-ready data assets for fraud detection and risk analytics.
This role requires individuals with solid communication, problem-solving, and independent thinking skills.
Preferred Platform Experience:
Experience working on AWS (or similar platform such as Azure / GCP)
Experience developing data pipelines using PySpark
Experience working with Neo4j and graph data modeling concepts
Familiarity with feature stores, embeddings,
and entity resolution techniques
Essential – Experience engineering and deploying production-grade data features or pipelines in an enterprise workplace
Essential – Experience with graph analytics, knowledge graphs, embeddings, or network-based modeling techniques
Nice to Have – Experience with Fraud Analytics, Strategy, Modeling, or Financial Crimes use cases
Languages and Tools:
Advanced (Essential) – Python