Facility: 1800 Parkway Center

Job Summary:

The AI Product Analyst at Wellstar Health System will support the evaluation, implementation, and optimization of AI-driven solutions across clinical, operational, and administrative functions. This role will be part of the AI Operations Team in the AI Center of Excellence (COE) at Wellstar, working closely with AI engineers, data scientists, IT, clinicians, and business stakeholders to ensure AI solutions align with organizational priorities, regulatory requirements, and governance policies. The AI Product Analyst will be responsible for analyzing AI solution feasibility, conducting risk assessments, tracking AI performance, and facilitating AI adoption across Wellstar. The AI Product Analyst will work closely with the Executive Director of Artificial Intelligence to ensure AI products meet organizational goals and user needs. The ideal candidate will have experience in AI product analysis, business intelligence, data-driven decision-making, and stakeholder engagement within a healthcare or regulated environment.

Core Responsibilities and Essential Functions:

Assess new AI proposals to determine feasibility, impact, and alignment with Wellstars AI strategy. Support AI COE in conducting AI risk assessments and assist in preparing AI governance reports for executive review. Track post-deployment AI performance, monitoring model accuracy, bias, and effectiveness. Perform data-driven assessments to measure AIs impact on operational efficiency, clinical workflows, and financial performance. Act as the liaison between AI teams, IT, clinical, and operational stakeholders, ensuring AI adoption aligns with end-user needs. Facilitate change management and user acceptance testing (UAT) for AI solution deployment. Develop dashboards and reports to visualize AI performance trends. Performs other duties as assigned Complies with all Wellstar Health System policies, standards of work, and code of conduct.

Required Minimum Education:

• Bachelors Computer Science or Masters Artificial Intelligence-Preferred

Required Minimum License(s) and Certification(s):

All certifications are required upon hire unless otherwise stated.

Additional License(s) and Certification(s):

Required Minimum Experience:

Minimum 5 years experience in technical product management, AI product analysis, business analytics, or data science. Required Experience working with AI-driven products, machine learning applications, data pipelines, or analytics platforms in healthcare, life sciences, technology, or a regulated industry. Required Familiarity with AI governance frameworks, risk management, and regulatory compliance requirements (e.g. HIPAA, FDA AI/ML regulations, ISO/IEC 42001, NIST AI standards, etc.). Required Experience supporting AI solution lifecycle management, from intake and risk assessment to deployment and post-implementation monitoring. Required Business & Product Analysis: Experience working as a Product Analyst, Business Analyst, Product Owner, or Data Product Manager in AI-driven environments. Preferred Healthcare & AI Integration: Experience working with healthcare data models, EHR integration, and interoperability standards (FHIR, HL7, DICOM, etc.). Preferred User Acceptance Testing & AI Performance Monitoring: Experience conducting user testing, tracking AI effectiveness, and supporting AI model adjustments. Preferred

Required Minimum Skills:

Ability to translate complex AI and data science concepts into actionable insights for business and clinical stakeholders. Data & Technical Expertise: Hands-on experience with SQL, Python, R, Power BI, Tableau, or Looker for AI product analytics. AI Governance & Compliance: Understanding of AI model validation, bias mitigation, and post-deployment monitoring in regulated settings. Stakeholder Engagement: Proven ability to collaborate with AI teams, IT, clinicians, and leadership to drive AI adoption. Agile & Product Development Methodologies: Familiarity with Agile, Scrum, or Lean methodologies in AI product lifecycle management. Strong background in data analysis, data visualization, and performance tracking of AI-driven solutions.