Healthcare Economist, Applied Researcher
Role Details
- Full-Time
- Office Location: New York City (preferred)
Why Translucent
Healthcare providers drive $2.5 trillion in medical expenditures annually and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions.
Translucent is changing that. We're building the AI-native financial platform designed exclusively for healthcare, giving every finance team, department, and service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows.
Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we've already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we're just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world, this is the place.
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. We have incredible product-market fit and demand across diverse customer profiles. 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
To facilitate the development of these systems, you will:
- 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. Getting AI to do analyst-level revenue cycle work requires a lot of things that look like analyst-level revenue cycle work
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
