Data Scientist Principal, AI Development and Governance

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.
• Strong Python and SQL skills, including experience performing feature engineering and analytical work within very large-scale data warehouses.
• At least 2 years of experience working with healthcare claims data, including Medicare, Medicaid, or commercial claims, together with working knowledge of medical terminology and coding systems such as ICD-10, CPT, HCPCS, and DRG.
• Experience presenting technical methodologies, model results, and governance decisions to clients, partners, auditors, or other stakeholders, with the ability to adapt explanations to both technical and non-technical audiences.
• Strong analytical and critical-thinking skills, with the ability to assess complex AI systems, identify risks, and establish practical standards for responsible deployment.
• Prior experience in a formal model-risk or responsible-AI role is desirable, including experience outside the healthcare sector.
• Experience with graph or network analytics, entity resolution, or record linkage is an advantage.
• Experience piloting generative AI tools in regulated or high-scrutiny environments is preferred.
• Experience with AWS and/or Snowflake environments, including Snowpark or model lifecycle tooling, is beneficial.
• Familiarity with payer coverage policies such as LCDs, NCDs, private carrier policies, and industry claim edits such as NCCI is a plus.
• No U.S. citizenship requirement and no security clearance is required for this position.

Benefits:

• Fully remote, full-time position with a distributed U.S. team.
• Estimated salary range of $119,000–$161,000, with actual compensation determined by experience, geographic location, and potentially contractual requirements.
• Full-flex work week designed to provide flexibility in managing work and personal priorities.
• Comprehensive medical plan options, including plans with Health Savings Accounts.
• Dental and vision coverage.
• 401(k) plan with company matching contributions.
• Paid time off, including vacation, sick, and personal leave, plus paid holidays.
• Typically 15 days of paid leave per calendar year for vacation, personal business, and illness, plus 10 paid holidays, subject to eligibility and prorating.
• Paid family leave of up to 160 hours in a rolling 12-month period for eligible employees.
• Paid parental, military, bereavement, and jury duty leave.
• Short- and long-term disability coverage, life insurance, accidental death and dismemberment coverage, personal accident, critical illness, and business travel and accident insurance.
• Tuition assistance and professional development opportunities.
• Internal mobility support and career development resources.
• Less than 10% travel expected.
• Opportunity to work on large-scale healthcare data, machine learning, generative AI, and responsible AI governance.