Healthcare Economics Domain Expert, Applied AI

Healthcare Economist, Applied Researcher Role Details Full-Time Office Location: New York City (preferred) Role Overview We are looking for a healthcare economist with deep expertise in revenue cycle management, reimbursement economics, payer‑provider dynamics, and the financial operations of enterprise healthcare organizations. Executing on this demand requires someone who understands the full lifecycle of healthcare revenue, from charge capture to final payment, and can translate that expertise into AI‑powered systems that help finance teams move from reactive reporting to proactive decision‑making. What You’ll Do Develop and deliver subject‑matter expertise in healthcare economics and revenue cycle management to support AI research, including reimbursement modeling, denial economics, payer contract analysis, and net revenue optimization. Build analytical frameworks for the full revenue cycle: charge capture efficiency, coding accuracy and its financial impact, claims submission and adjudication patterns, denial root cause analysis, and collections performance. Model payer‑provider economics, including contract rate analysis, fee schedule benchmarking, allowable vs. billed variance, and the financial impact of payer mix shifts. Develop frameworks for understanding reimbursement mechanics across Medicare (IPPS, OPPS, physician fee schedule), Medicaid, managed care, and commercial payers. Analyze the economics of clinical operations, including physician productivity, procedure‑level profitability, service line contribution margins, and cost‑to‑collect ratios. Work closely with our engineering, product, and design teams to translate healthcare economic logic into production code and AI Agents. Partner directly with customers to understand their revenue cycle pain points, identify financial leakage, and translate complex reimbursement and operational requirements into technical solutions. Build proprietary benchmarks and datasets to evaluate models and AI Agents against real‑world healthcare finance tasks, including denial rate analysis, days in A/R trending, net collection rate modeling, and payer performance scoring. What You Have 5–10 years of experience in healthcare economics, revenue cycle management, healthcare consulting, or an equivalent function within a health system, medical group, or payer organization. Deep understanding of healthcare reimbursement: how providers get paid, what drives variation in payment, and where revenue leaks across the cycle. Strong command of revenue cycle KPIs: denial rates, days in A/R, clean claim rates, net collection rates, cost to collect, and how these metrics connect to financial performance. Familiarity with Medicare and Medicaid reimbursement methodologies (DRGs, APCs, RBRVS, etc.) and commercial contract structures. Experience analyzing claims data, remittance files, charge masters, or payer contracts to identify financial trends and opportunities. Ability to effectively communicate with a variety of internal and external stakeholders and translate complex healthcare economic problems between finance, product, and engineering teams. Ability to define positive outcomes in situations with underspecified success criteria. Deep intellectual curiosity and eagerness to learn across domains, particularly at the intersection of healthcare economics and AI. Willingness and desire to do work in the trenches: grading hundreds of model‑generated denial analyses, breaking down thousands of remittance records, stress‑testing reimbursement models against real‑world payer behavior. Nice to Have Experience working inside a health system revenue cycle or finance department, not just consulting to one. Familiarity with SQL, Python, or other data tools. Background in healthcare price transparency, chargemaster optimization, or payer contract negotiation strategy. Experience with value‑based care payment models and their impact on revenue cycle operations. Knowledge of healthcare regulatory economics: No Surprises Act, site‑of‑service differentials, 340B, etc. Prior exposure to AI/ML concepts or prompt engineering. Experience at a high‑growth startup. Anticipated compensation: $175,000 - $250,000 with Equity #J-18808-Ljbffr