Staff Data Scientist for Claims Payment ML
Opportunity Summary
You will own end-to-end data science work for claims payment products, starting from problem framing and data exploration through modeling, validation, and production impact. The role includes driving high-impact initiatives and advancing models that detect and stop payment errors in daily production. In addition to building models, you will create technical frameworks and tooling so the team can scale more effectively and consistently achieve business outcomes. Success requires strong data science fundamentals, hands-on ML execution, and the ability to build reusable systems for a production environment.
Ideal Candidate
A senior to expert data scientist who has shipped production ML systems and built internal tooling that improves scale. You bring strong statistics, robust feature engineering, and a practical approach to validation and monitoring in healthcare settings.
Project Insights
- Healthcare payment detection benefits from strong error analysis and feedback loops
- Reusable training and validation frameworks can dramatically increase throughput
- Monitoring and drift detection are critical for daily operational models
How to Prepare
- Prepare a production case study showing measurable reduction in errors or improved detection rates
- Explain your validation strategy, including handling class imbalance and label quality
- Discuss how you build frameworks that let teams iterate quickly while keeping models reliable
Typical Challenges
- Improving model accuracy under messy, real-world claims data conditions
- Bridging offline modeling with reliable production deployment and monitoring
- Scaling experimentation and vendor or pipeline integrations without regressions
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Project Details
Skills & Requirements
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