Data Scientist Principal, AI Development and Governance

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist Principal, AI Development and Governance based in United States. This is a fully remote, full-time senior individual-contributor role focused on building trustworthy AI and machine learning capabilities for healthcare fraud, waste, and abuse detection. You’ll split your time between developing production-ready models and establishing the standards that guide responsible AI across the broader data science team. The role combines hands-on machine learning, generative AI evaluation, model governance, and large-scale healthcare data analysis. You’ll work with complex claims data to develop findings that can be trusted by healthcare partners, investigators, and auditors. The position also requires the ability to assess emerging AI capabilities, determine where they can add value, and identify situations where they are not yet appropriate. You’ll collaborate with data scientists, BI developers, subject matter experts, partners, and auditors across a distributed U. S. team. This opportunity is well suited to an experienced data scientist who enjoys both technical delivery and setting rigorous standards for responsible AI. Accountabilities: - Establish modeling and validation standards for the Data Science team, including expectations for model documentation, monitoring, drift detection, bias assessment, and production readiness. - Review data science models against established standards before production deployment and provide recommendations on the highest-priority improvements required for quality, reliability, and governance. - Develop and maintain responsible-AI and generative-AI policies covering both customer-facing or investigator-facing use cases and internal AI-enabled development tools.

- Build and deploy machine learning models for healthcare fraud, waste, and abuse detection, including supervised risk scoring and feature engineering across large-scale claims data. - Validate models under significant class imbalance and evolving fraud patterns, ensuring methodologies remain appropriate as new evidence and behaviors emerge. - Evaluate potential generative AI applications for feasibility, reliability, risk, and suitability within a highly scrutinized healthcare environment, including recommending against adoption when a use case is not sufficiently mature. - Explain model methodologies, validation results, governance controls, and AI-assisted processes to healthcare partners, internal stakeholders, and auditors. - Defend technical and governance decisions to both highly technical reviewers and stakeholders without specialized data science backgrounds. - Collaborate with Data Scientists, BI Developers, and FWA Subject Matter Experts across a fully distributed U. S. team, serving as a key resource for governance and AI-related questions. - Contribute to the continuous improvement of technical standards, governance practices, and AI capabilities as the organization’s data science environment evolves. Requirements: - Master’s degree in statistics, computer science, engineering, applied mathematics, economics, or another quantitative discipline, or a bachelor’s degree in a related quantitative field combined with equivalent hands-on experience. - 8+ years of experience building, validating, and deploying machine learning models using real-world data, including experience establishing technical standards for other data scientists. - Strong working knowledge of responsible AI and model-risk practices, including model documentation, monitoring, bias and drift detection, validation, and production governance. - Demonstrated experience evaluating generative AI and LLM use cases for both technical feasibility and risk, including the ability to determine when an LLM should not yet be used for a particular application.