Healthcare Actuary, 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 actuary with deep expertise in how health systems and medical groups evaluate risk, price contracts, forecast costs, and navigate the shift toward value-based care. We have incredible product-market fit and demand across diverse customer profiles. Executing on this demand requires someone who understands actuarial methodology inside and out — and can translate that expertise into AI-powered systems that scale across hundreds of healthcare organizations.
What You'll Do
To facilitate the development of these systems, you will:
- Develop and deliver subject-matter expertise in healthcare actuarial science to support AI research — including risk stratification, financial forecasting, payer mix analysis, and reimbursement modeling
- Build forward-looking forecasting models for healthcare organizations: cost trend projections, utilization forecasts, revenue forecasts under different payer and contract scenarios, and budget variance prediction
- Develop frameworks for evaluating value-based care performance, including shared savings/losses, capitation economics, ACO financial benchmarking, and risk corridor analysis
- Model healthcare pricing and risk, including rate-setting assumptions, medical loss ratios, utilization patterns, and population health cost drivers
- Build revenue cycle analytics models covering denial rate drivers, net collection forecasting, contractual adjustment analysis, and payer contract performance
- Work closely with our engineering, product, and design teams to operationalize actuarial logic — including forecasting engines — into production code and AI Agents
- Support risk-bearing entity analysis — helping health systems understand their exposure across commercial, Medicare Advantage, Medicaid managed care, and direct contracting arrangements
- Partner directly with customers to validate assumptions, stress-test forecasts, and translate complex actuarial and risk concepts for finance audiences
- Build proprietary benchmarks and datasets to evaluate models and AI Agents against real-world actuarial tasks — including cost trending, risk scoring, reserve estimation, and contract modeling
What You Have
- ASA or FSA designation (or near-credentialed with a clear path)
- 5–10 years of experience in healthcare actuarial work — health plan pricing, provider risk, Medicare/Medicaid, commercial lines, or value-based care arrangements
- Proven experience building financial forecasts in healthcare: cost trends, utilization projections, revenue modeling, or reserve development
- Deep understanding of risk-based payment models: capitation, shared savings, bundled payments, risk adjustment (HCC/RAF scoring), and stop-loss structures
- Strong command of claims data, utilization metrics, cost of care analytics, and healthcare reimbursement mechanics
- Ability to effectively communicate with a variety of internal and external stakeholders and translate complex actuarial 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 actuarial science and AI
- Willingness and desire to do work in the trenches — e.g., grading hundreds of model-generated forecasts, breaking down thousands of claims files, stress-testing risk models against real-world standards. Getting AI to do actuary-level healthcare finance work requires a lot of things that look like actuary-level healthcare finance work
Nice to Have
- Experience on the provider side — working with health system finance teams evaluating risk-bearing contracts, not just payer-side reserving
- Familiarity with SQL, Python, or other data tools
- Background in building scenario-based or Monte Carlo–style forecasting models
- Background in Medicare Advantage bid development, MSSP/ACO REACH benchmarking, or Medicaid managed care rate-setting
- Prior exposure to AI/ML concepts or prompt engineering
- Experience at a high-growth startup
Anticipated compensation: $175,000 - $250,000 with Equity
