AI Product Business Analyst

Omega Healthcare Solutions•United States,

4d•$114,000 - $130,000•Remote

About The Position

The AI Product Business Analyst will serve as the vital link between our clinical operations teams and our AI development team. This position will be responsible for creating complex medical coding requirements from subject matter experts and translating them into actionable user stories and functional specifications for our Agentic AI engine. The goal is to ensure that the “logic” the AI learns is perfectly aligned with industry standards and specific client needs.

Requirements

Bachelor’s degree in business, computer science, health informatics, or a related field.
5+ years as a business analyst, with at least 2 years focused on AI, machine learning, or advanced analytics products.
Exceptional skill in writing user stories within an Agile framework, specifically for complex logic-based systems.
Solid understanding of how AI models are trained and the difference between deterministic logic and probabilistic AI outputs.
Expert proficiency in Jira, Confluence, and wireframing/flowcharting tools (e.g. Lucidchart, Visio).

Nice To Haves

Experience in healthcare, RCM, or medical coding is highly preferred.
Ability to write basic SQL queries to analyze data sets for requirement validation.
CSPO (Certified Scrum Product Owner) or similar BA certifications.
Understanding of the prompts and “instructions” needed to guide Large Language Models.

Responsibilities

Conduct workshops with Clinical SMEs and RCM experts to document specialty-specific coding rules and nuances.
Write detailed user stories, acceptance criteria, and functional requirements specifically for AI/ML features (e.g. The model must identify unbundled procedures in cardiology charts).
Translate high-level business goals into technical requirements that include data labeling instructions, expected model outputs, and edge-case handling.
Define and execute User Acceptance Testing (UAT) strategies to ensure the AI engine’s output meets the documented business requirements.
Manage and prioritize the product backlog, ensuring the AI development team always has a “definition of ready” for the next sprint.
Identify discrepancies between current manual coding workflows and the proposed AI-automated states, proposing logic-based solutions to bridge the gap.
Assist in mapping clinical data elements from various HER/EMR systems to the standardized inputs required by the auto-coding engine.