Senior Denials SME Operator

Overview

Thank you for considering a career at Ensemble Health Partners!

Ensemble Health Partners is a leading provider of technology-enabled revenue cycle management solutions for health systems, including hospitals and affiliated physician groups. They offer end-to-end revenue cycle solutions as well as a comprehensive suite of point solutions to clients across the country.

Ensemble keeps communities healthy by keeping hospitals healthy. We recognize that healthcare requires a human touch, and we believe that every touch should be meaningful. This is why our people are the most important part of who we are. By empowering them to challenge the status quo, we know they will be the difference!

The Senior Denials SME Operator is a senior-level individual contributor responsible for identifying true denial root causes, designing actionable taxonomies and prevention models, and validating AI-assisted appeal generation and predictive denial approaches.

Job Summary

The Senior Denials SME Operator serves as a subject matter expert in specific revenue cycle domains while maintaining literacy across the end-to-end revenue cycle. They redesign workflows, identify upstream root causes, build knowledge graphs, participate in design sessions, and validate emerging automation and AI-driven models.

Core Responsibilities:

• Act as the domain SME while maintaining literacy across the full revenue cycle.
• Identify structural root causes that drive downstream rework, friction, and leakage.
• Design future-state workflows and operating models with measurable impact.
• Build and maintain domain-specific knowledge graphs capturing inputs, rules, actions, and outcomes.
• Participate in design sessions and rapid innovation sprints with product, engineering, and analytics teams.
• Test and validate automation, ML, AI, and agentic models using real operational scenarios.
• Translate SME judgment into rules, decision trees, training data, and guardrails.
• Support pilots and controlled rollouts with clear success criteria and measurement.
• Document operational logic and findings for reuse and scale.

Role-Specific Responsibilities:

• Identify true root causes of denials and separate provider errors from payer behavior.
• Design more actionable denial taxonomies and prevention models.
• Build predictive models linking denial risk to upstream process failures.
• Test automated and AI-assisted appeal generation models.

Required Qualifications:

• 5+ years of hands-on experience in the revenue cycle domain aligned to this role.
• Strong end-to-end revenue cycle literacy.
• Exceptional process mapping and process improvement capability.
• Data-driven problem solving and comfort with variance analysis.
• Ability to operate effectively in ambiguity; self-motivated and proactive.
• Strong communication skills for both technical and non-technical audiences.
• Curiosity and aptitude for automation, ML/AI, and agentic technologies.
• This is a hybrid role based in Austin, Texas. Candidates must be able to commute to the Austin on-site location as required.

Senior-Level Individual Contributor Expectations:

• Demonstrated ability to lead problem-solving and redesign efforts independently (without formal authority).
• Proven ability to challenge existing processes constructively and propose more efficient methods.
• Comfort partnering closely with engineering/product teams to define, test, and iterate solutions.
• Ability to translate complex operational judgment into structured logic suitable for automation or AI.

• Innovative thinking and solutioning mindset
• Creative problem solving and systems thinking
• Comfort challenging existing processes to identify better, more efficient methods
• Interest in and practical familiarity with AI-enabled workflows and automation
• Strong stakeholder communication and collaboration

This role operates in a rapid-iteration environment and requires comfort with ambiguity, experimentation, and cross-functional collaboration. The Senior Operator is expected to challenge existing processes respectfully, propose improvements, and help validate automation and AI-driven solutions using real operational scenarios.