Subject Matter Expert, Clinical Operations & Throughput

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

Throughput opportunities are easy to compute but complicated to understand: a modeled length-of-stay reduction only becomes margin under conditions a spreadsheet can’t predict. As our subject matter expert for this domain, you will bring that judgment into the product itself, shaping how our agents reason about flow, capacity, and utilization, and holding model output to the standard a working operations leader would apply.

This position reports to Adam Albrecht, Head of Domain Intelligence.

WHAT YOU'LL DO

To facilitate the development of these systems, you will:

Develop and deliver subject matter expertise in clinical operations and patient flow 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 throughput and capacity workflows

Build proprietary benchmarks and datasets to evaluate models and AI agents against real-world clinical operations tasks including, but not limited to: length-of-stay variance analysis, operating room block utilization and release behavior, emergency department flow, imaging and lab utilization review, and discharge barrier identification

Partner with customer delivery to understand our customer’s capacity and flow operations, identify pain points, and translate complex clinical and operational requirements into technical solutions

WHAT YOU HAVE

7+ years in clinical operations, patient flow, perioperative services, or an equivalent function, ideally with exposure to length of stay, clinical utilization, and/or capacity management

Strong proficiency with clinical operations and data systems typically used by provider organizations (Epic, financial platforms, analytic/reporting tools, Excel) and fluency in throughput metric definitions and their common failure modes

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, both clinical and financial, and translate complex problems between operations, 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 clinical operations and AI

Willingness and desire to do work in the trenches — e.g., grading hundreds of model-identified discharge barriers against what actually held the patient, hand-auditing OR cases for true block release behavior, pressure-testing whether a modeled capacity gain would survive a real census week. Getting AI to reason about flow requires a lot of reasoning about flow

NICE TO HAVE

Experience at a high-growth startup, or across both academic and community settings

Familiarity with SQL, Python, or other data tools

Prior exposure to AI/ML concepts or prompt engineering

Anticipated compensation: $175,000 – $250,000 and Equity