- Experience in writing ETL scripts in Scala and Spark.

- Hands-on experience in Quantexa

- Worked on Fraud use cases

- Good understanding of git, jira for deployment management and communication skills to operate within the team

Responsibility of / Expectations from the Role :

- Build and configure Quantexa-based fraud models using entity resolution, graph analytics, scoring models, and risk indicators.

- Develop detection logic aligned with insurance fraud typologies such as staged accidents, inflated claims, synthetic identities, mule networks and collusive networks.

- Implement contextual monitoring to surface hidden relationships across claimants, brokers, vehicles, service providers, employees, and external parties.

- Build and optimize Spark-based data pipelines for claims, policy, billing, FNOL, external intelligence and investigative data.

- Enhance investigative workflows by providing contextual network views for adjusters, SIU teams, and fraud investigators.

- Partner with SIU, Claims Operations, Underwriting Fraud teams to validate typologies and refine detection thresholds.

- Tune Quantexa components such as entity resolution configuration, rule scoring, risk indicators and alert prioritization.