Subject Matter Expert, Workforce & Labor Productivity

Role Details

• Full-Time
• Office Location - New York City (preferred)

Role Overview

Labor is the largest line on most provider income statements and among the hardest to reason about, because a staffing variance can be a model problem, a demand problem, or a byproduct of the competitive landscape. As our subject matter expert for this domain, you will bring that judgment into the product itself, shaping how our agents reason about premium labor, staffing to census, and productivity standards, while holding model output to the standard a healthcare workforce 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 healthcare workforce and productivity management 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 workforce and labor workflows
• Build proprietary benchmarks and datasets to evaluate models and AI agents against real-world workforce tasks including, but not limited to: agency and premium labor analysis, staffing-to-census matching, productivity benchmarking, overtime and incentive-shift attribution, and schedule variance review
• Partner with customer delivery to understand our customer's staffing and scheduling cycles, identify pain points, and translate complex operational and financial requirements into technical solutions

What You Have

• 7+ years in healthcare workforce operations, nursing operations, staffing management, or an equivalent function, ideally with exposure to premium labor, productivity standards, and labor budgeting
• Strong proficiency with time-and-attendance and scheduling systems (UKG/Kronos, API Healthcare, or similar) and labor benchmark sources (Premier, Vizient, or similar), plus fluency in productive versus non-productive hours and worked-hours-per-unit-of-service definitions
• 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 workforce 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 workforce management and AI
• Willingness and desire to do work in the trenches - e.g., grading model-flagged staffing variances against what a manager actually faced that shift, reconstructing schedules to trace where premium pay originates, checking flex decisions against census by hand. Getting AI to reason about staffing requires a lot of reasoning about staffing

Nice to Have

• Experience at a high-growth startup, or across multiple labor markets and union environments
• 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