Principal Engineer, Data Insights (REMOTE)

EnableComp • Franklin, TN

1d • Remote

About The Position

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.

Nice To Haves

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level