Healthcare Analytics, AI Product & Methodology Analyst

IQVIA•Greenwich, CT

3d•Remote

About The Position

The Healthcare Analytics, AI Product & Methodology Analyst at Cedar Gate Technologies, an IQVIA business, will help evaluate how AI can be effectively applied to healthcare analytics and provider performance insights. This role focuses on identifying opportunities to leverage generative AI and other emerging technologies to improve how healthcare data is analyzed, interpreted, and delivered to end users. The ideal candidate has a foundation in healthcare analytics, data analysis, or a related field and is eager to explore the practical application of AI within healthcare. Working closely with product, analytics, methodology, and AI/ML engineering teams, this individual will help define requirements, assess analytical outputs, and contribute to the development of AI-enabled products. This role is well suited for a developing analyst who enjoys solving complex problems, learning new technologies, and bridging business, data, and product perspectives to create meaningful solutions.

Requirements

Bachelor's degree in Health Informatics, Public Health, Healthcare Administration, Economics, Statistics, Data Analytics, Biostatistics, Computer Science, or a related field.
At least 1 year of experience in healthcare analytics, medical economics, provider or payer analytics, health services research, or a related analytical discipline; 3+ years is preferred.
Familiarity with healthcare data and performance measurement concepts, including medical and pharmacy claims, provider reporting, quality measures, and cost or utilization analytics.
Strong analytical and problem-solving skills, with the ability to evaluate data, identify trends, ask thoughtful questions, and draw meaningful conclusions.
Demonstrated intellectual curiosity, learning agility, and interest in exploring new technologies and approaches to solving business problems.
Ability to translate complex analytical findings into clear, actionable insights for business partners and stakeholders.
Strong written and verbal communication skills.
Ability to collaborate effectively with cross-functional teams, including analytics, product management, data science, and technology partners.
Proficiency in SQL and Excel; experience with Python, R, Power BI, Tableau, or similar analytical tools is preferred.
To be eligible for this position, you must reside in the same country where the job is located.

Nice To Haves

Exposure to provider performance measurement, healthcare reporting methodologies, attribution models, or quality measurement frameworks is preferred.
Experience working in a payer, provider, healthcare technology, or healthcare consulting environment is preferred.
Exposure to predictive analytics, AI/ML solutions, generative AI tools, or data-driven product development is preferred.

Responsibilities

Support the development and evaluation of AI-enabled healthcare analytics solutions, helping define business and analytical requirements for tools that deliver provider cost, utilization, and quality insights.
Collaborate with product, analytics, and AI/ML teams to translate user needs, reporting requirements, and business objectives into effective analytics and AI-driven capabilities.
Review and assess AI-generated outputs to ensure alignment with healthcare reporting methodologies, measure definitions, and user expectations.
Participate in testing and validation activities, including identifying discrepancies, documenting issues, and helping determine whether solutions meet business and analytical requirements.
Analyze the impact of changes to clinical groupers, attribution methodologies, and reporting approaches on provider performance measurement and healthcare analytics outputs.
Compare current and proposed methodologies, identify meaningful differences in results, and help evaluate potential impacts on trends, benchmarks, and stakeholder interpretation.
Provide analytical and healthcare domain feedback on AI models, predictive outputs, and emerging product features to support solution quality, usability, and business relevance.
Identify data quality concerns, unexpected results, and opportunities for improvement, escalating complex methodological or interpretation issues when appropriate.
Prepare clear documentation, analyses, and summaries that communicate findings, assumptions, recommendations, and testing results to cross-functional stakeholders.