Subject Matter Expert, Managed Care & Payor Contracting

Translucent•New York, NY

17h•$175,000 - $250,000•Onsite

About The Position

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. Contract terms determine what a health system is owed, but that language lives in amendments, appendices, and institutional memory rather than anywhere a system can easily query. As our subject matter expert for this domain, you will bring that judgment into the product itself, shaping how our agents read contract language, model expected reimbursement, and monitor payor behavior, while holding model output to the standard a working contracting leader would apply.

Requirements

7+ years in managed care contracting, payor strategy, network management, or an equivalent function, ideally with exposure to rate modeling, term negotiation, and payor performance monitoring
Strong proficiency with contract modeling and management tools (Epic contracts, nThrive, Strata, or similar) and Medicare fee-schedule mechanics (PFS, OPPS, IPPS) as the reference most commercial terms are built against
Ability to convert workflows and hypotheses into structured data, rules, and logic for algorithms and quality measurement as well as the expertise to link data points that may not already speak to each other in ways that enhance performance
Demonstrated ability to deliver high-quality analyses on demanding deadlines
Ability to effectively communicate with a variety of internal and external stakeholders and translate complex problems between contracting, legal, 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 payor contracting and AI
Willingness and desire to do work in the trenches — e.g., reading contract amendments to build a structured clause taxonomy, grading model-extracted terms line by line, reconciling paid-versus-expected at claim level to prove a model got it right. Getting AI to read a contract like an operator requires a lot of reading contracts like an operator

Nice To Haves

Experience at a high-growth startup, or on both the provider and payor side
Familiarity with SQL, Python, or other data tools
Prior exposure to AI/ML concepts or prompt engineering

Responsibilities

Develop and deliver subject matter expertise in managed care contracting and payor strategy to support AI product development and customer margin realization
Work closely with our engineering, product, and design teams to define and develop AI systems for contracting and payor performance workflows
Build proprietary benchmarks and datasets to evaluate models and AI agents against real-world contracting tasks including, but not limited to: contract clause extraction and classification, expected-reimbursement modeling, payor policy monitoring, value-based contract reconciliation, and renewal scenario analysis
Act as the company’s direct line of insight into upcoming policy changes across the managed care landscape
Partner with customer delivery to understand our customer’s contracting cycles, identify pain points, and translate complex contractual and financial requirements into technical solutions