Regulatory Policy Analyst: Clinical AI/ML Support (PRN, Remote)

PRN, Remote

http://www.integritym.com

About Us

Integrity Management Services, Inc. (IntegrityM) is an award-winning, women-owned small business specializing in assisting government and commercial clients in compliance and program integrity efforts, including the prevention and detection of fraud, waste and abuse in government programs. Results are achieved through data analytics, technology solutions, audit, investigation, and medical review.

At IntegrityM, we offer a culture of opportunity, recognition, collaboration, and supporting our community. We thrive off of these fundamental elements that make IntegrityM a great place to work. Our small, flexible workplace offers an exceptional quality of life and promotes corporate-driven sustainability. We deliver creative solutions that exceed goals and foster a dynamic, idea-driven environment that nurtures our employees’ professional development. Large company perks…Small company feel!

Position Overview

The Regulatory/Policy Analyst provides subject matter expertise in federal, state, and commercial payer policies, with a focus on supporting artificial intelligence and machine learning (AI/ML) model development. The role blends translating complex regulatory frameworks into actionable insights that guide model training, validation, and ongoing performance monitoring. This role serves as a bridge between regulatory compliance, clinical expertise, and data science to ensure model integrity and audit readiness.

Key Responsibilities

Regulatory and Policy Interpretation

• Monitor, analyze, and interpret evolving federal, state, and commercial healthcare regulations, coding guidance, and policy updates.
• Translate complex regulatory language into clear, actionable insights to inform AI/ML model design and application.
• Act as a regulatory policy tie-breaker and escalation resource for model development and review discussions.

Model Development and Support

• Provide clinical and regulatory input to support the training, testing, and validation of AI/ML models, ensuring alignment with coding and policy standards.
• Contribute to quality assurance and model performance evaluation efforts, including inter-rater reliability (IRR) assessment and policy-level consensus activities.
• Assist in the development of compliance safeguards and regulatory checkpoints within the model lifecycle.

Training and Communication

• Design and deliver internal training, level-setting workshops, and documentation to ensure stakeholder understanding of regulatory frameworks.
• Serve as a knowledge resource for audit-readiness and policy compliance during external reviews or model validations.

Requirements

Required Qualifications

• Bachelor’s degree in Nursing, Health Policy, Healthcare Administration, or a related field.
• Minimum of 3 years of clinical experience as a registered nurse.
• At least 6 years of experience in regulatory compliance, healthcare policy analysis, medical auditing, or appeals within healthcare settings.
• Demonstrated experience interpreting and applying federal and state healthcare regulations and payer policies.
• Strong analytical skills and experience working with cross-functional teams, particularly in data science, informatics, and quality assurance.
• Excellent written and verbal communication skills; ability to translate technical and regulatory language into accessible guidance.
• Experience supporting AI/ML-enabled healthcare tools, data-driven models, or digital health applications is a plus.
• High attention to detail, with the ability to manage competing priorities in a fast-paced environment.

Preferred Qualifications

• Active Registered Nurse (RN) license preferred.
• Or other clinical specialties.
• Master's Degree in Nursing, Health Policy, Healthcare Administration, or a related field preferred.
• Familiarity with CMS regulations, HEDIS, NCQA, or other performance measurement frameworks.
• Experience with commercial/private insurance, Medicare Advantage (Part C) policy and/or CMS program integrity initiatives
• Prior involvement in AI/ML model development, clinical data analytics, or technology-driven healthcare tools
• Familiarity with healthcare data structures such as claims, EHR, and HEDIS metrics
• Understanding of algorithmic bias and mitigation practices in healthcare modeling
• Knowledge of machine learning concepts, model governance, or compliance documentation in digital health or health IT settings.