Source description
About the role
Technical & Solution Leadership
Design, develop, and review machine learning solutions across insurance domains, including claims, underwriting, sales and marketing.
Take ownership of:
Feature engineering using large-scale insurance datasets
Model selection, training, validation, and performance tuning
Handling highly imbalanced datasets, weak labels, and proxy targets
Translating business rules into ML features / hybrid rule-ML systems
Ensure model explainability, stability, and governance aligned with insurance and regulatory expectations (e.g., interpretable ML, bias mitigation).
- Stakeholder & Program Collaboration
Act as the client facing data scientist who manages client relationships
Prepare demos and sprint review materials
Translate high-level business problems into:
Well-defined analytics use cases
Modeling approaches and delivery plans
Participate in:
Architecture and solution design discussions
Model walkthroughs with technical and business stakeholders
UAT discussions and model acceptance criteria definition
Communicate risks, dependencies, and delivery trade-offs early and clearly.
- Data, Platform & MLOps Alignment
Work with data engineering and platform teams to:
Shape analytical data models and feature stores
Ensure production readiness of models
Contribute to:
MLOps design (model versioning, monitoring, retraining strategies)
Deployment patterns on modern analytics platforms (e.g., cloud-based data & ML stacks)
Ensure models meet enterprise standards for scalability, reliability, and auditability.
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