Principal Engineer, Data Insights (REMOTE)

EnableComp•United States - Remote, TN

5h•Remote

About The Position

Our Vision is to empower healthcare providers to focus on patient care, not revenue cycle complexity. Our Mission is to uncover what others miss—turning every client dollar recovered into insight that helps prevent future loss. We live our Core Values of Uncompromising Integrity , Ecstatic Clients , and Empowered Team Members by operating with trust, accountability, and a “we before me” mindset. We pursue Innovation and Profitable Growth through continuous improvement and thoughtful problem-solving, and we believe work should include Fun —celebrating success, recognizing our people, and maintaining healthy work-life balance. POSITION SUMMARY The Principal Engineer, Data Insights, is responsible for leading the hands-on development of EnableComp’s predictive intelligence platform, transforming our Databricks lakehouse from data storage into a revenue-generating prediction engine. As the Principal Engineer, Data Insights, you will own ML model development for multi-dimensional work prioritization, business insights, and strategic build-vs-partner decisions that determine how we scale our predictive capabilities. This is a 100% hands-on technical leadership role where you will architect and build the platform before scaling a team.

Requirements

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
Must have 6-8+ years deploying production ML systems, preferably in financial services or healthcare domains
Deep hands-on expertise with Databricks, MLflow, Spark optimization, and distributed computing architectures
Modern ML frameworks: Python, scikit-learn, XGBoost, LightGBM, deep learning libraries.
Statistical modeling: Time series forecasting, ensemble methods, feature engineering at scale, survival analysis, causal inference
Production deployment: Real-time scoring APIs, model monitoring, A/B testing frameworks
Data engineering at scale: Unity Catalog governance, Delta Lake ACID transactions, streaming ingestion, schema evolution management
Complex claims domain knowledge (VA, Workers’ Comp, MVA, Out-of-State Medicaid)
Understanding of revenue cycle workflows and predictive opportunities
Healthcare data experience: claims processing, payer behavior, denial patterns, medical coding
Azure ML ecosystem and cloud infrastructure
Data governance and HIPAA compliance requirements
API design for cross-functional consumption
Medallion architecture implementation (Bronze/Silver/Gold layers)
Timely and regular attendance.
Equivalent combination of education and experience will be considered
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily.
Reasonable accommodations may be made to enable qualified individuals with disabilities to perform the essential functions.
Practices and adheres to EnableComp’s Core Values, Vision and Mission
Exceptional technical leadership driving innovation in emerging technologies
Ability to translate abstract AI capabilities into concrete RCM solutions
Experience managing mixed onshore/offshore development teams
Track record of delivering complex platforms that scale across business units

Responsibilities

Real-time ML scoring infrastructure processing 10K+ claims daily with <2-second latency
Feature engineering pipelines transforming raw claims, payer contracts, fee schedules, and partner data into predictive signals
Multi-dimensional "super scoring" system combining contract variance, denial probability, and recovery likelihood across internal + partner datasets
Model deployment architecture supporting A/B testing, automated retraining, and agentic development workflows
MLflow model registry managing versioning, lineage, and deployment pipelines
Unity Catalog implementation governing model access and data lineage
Medallion architecture (Bronze/Silver/Gold) for analytics-ready data
Work Prioritization & Super Scoring: Multi-dimensional scoring combining partner intelligence (denial scoring) with internal production data and learnings
Portfolio Optimization: Cash flow forecasting, recovery probability modeling, capacity planning
Contract Intelligence: Variance detection, underpayment prediction (evaluate build vs partnership)
Additional Insights: Identify and develop predictive opportunities as platform matures
Design and personally build ML platform architecture on Databricks/Azure from the ground up
Develop production-ready predictive models from concept through deployment
Implement distributed computing optimization for processing millions of historical claims
Build automated model monitoring, retraining triggers, and performance dashboards
Leverage agentic development frameworks for rapid model iteration
Own end-to-end ML pipeline: feature engineering, model training, production deployment, monitoring
Achieve <2-second latency for real-time scoring at scale
Design APIs enabling consumption across operations, finance, and product teams
Integrate denial scoring intelligence into super-scoring framework
Own integration architecture whether building internally or partnering externally