Assistant Manager - FWA Data Science

• Develop explainable detection methodologies.

FWA Data Science is responsible for transforming validated FWA analytical prototypes into robust, scalable, and measurable detection models ready for production implementation. The role owns model enhancement, feature engineering, calibration, validation, explainability, performance assessment, and continuous model improvement throughout the solution lifecycle.

Working closely with Actuarial, Claims Integrity, and FWA AI Engineering, the role ensures FWA detection models remain effective, relevant, and aligned with evolving fraud patterns after deployment.

• 1
  - Model Industrialization Readiness;
  • Assess AI/ML prototypes received from Actuarial teams.
  • Refine model logic and improve statistical rigor.
  • Enhance feature engineering methodologies.
  • Validate and clarify prototype assumptions.
  • Develop explainable detection methodologies.
  • Support migration of approved analytical models into cloud-based solutions.
• 2
  - Detection Model Development & Enhancement;
  • Improve and retrain FWA models using new data and investigation outcomes.
  • Develop advanced fraud detection features.
  • Enhance anomaly detection models.
  • Build risk scoring methodologies.
  • Improve model precision and operational effectiveness.
• 3
  - Model Validation & Performance Science;
  • Define model success metrics.
  • monitor precision, recall, and business impact.
  • Perform false-positive and false-negative analysis after go-live
  • Validate model outputs with investigators.
  • Support production readiness reviews.
• 4
  - Fraud Intelligence & Pattern Evolution;
  • Analyze investigation outcomes.
  • Identify emerging fraud behaviors.
  • Design new detection features.
  • Recommend modifications to existing models.
  • Collaborate with FWA operations on evolving threats.
• 5
  - Continuous Model Optimization;
  • Monitor business effectiveness of deployed models.
  • Recommend retraining and recalibration activities.
  • Support periodic model reviews.
  • Maintain model documentation and governance artifacts.

• Python
• SQL
• SAS
• Machine Learning
• Statistical Analysis
• Feature Engineering
• Model Validation & Performance Analysis
• FWA / Fraud Analytics
• Healthcare Claims Analytics
• NLP & LLM Applications
• Google Cloud Platform (GCP)
• Cloud-Based Analytics Solutions
• SAS FWA
• Power BI / Data Visualization
• Problem Solving
• Stakeholder Management
• Communication & Presentation Skills
• Cross-Functional Collaboration
• Experience in health insurance, claims analytics, FWA, risk management, or financial crime analytics is preferred.
• Experience working with Google Cloud Platform (GCP) is preferred.
• Experience using SAS for statistical analysis and model assessment.

Bachelor's Degree in Computer Science, Data Science, Artificial Intelligence, or a related field