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.

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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.

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How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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