Director, Model Engineering & Operations

• Lead the design, development, and productionization of ML and AI models across risk adjustment (HCC/RAF), HEDIS/Stars quality measures, care management, utilization management, fraud/waste/abuse, and member/provider experience use cases.
• Own the end-to-end MLOps lifecycle on Databricks: feature engineering and feature store design, model training and versioning (MLflow), CI/CD for ML pipelines, deployment patterns (batch, real-time, and streaming inference), and automated retraining.
• Establish and enforce model monitoring practices — drift detection, performance degradation alerts, bias/fairness checks, and champion-challenger frameworks — to ensure production models remain accurate and compliant over time.
• Guide the evaluation and responsible adoption of generative AI and LLM-based capabilities (e.g., Mosaic AI, Genie, retrieval-augmented generation) for internal analytics, member/provider-facing tools, and operational automation.
• Define engineering standards, design patterns, and reusable components (feature libraries, model templates, deployment scaffolding) to accelerate delivery across the data science and ML engineering teams.
• Partner with data governance and security teams to ensure all ML/AI systems comply with HIPAA, CMS, and NCQA requirements, including PHI handling, access controls, and audit trails within Unity Catalog.
• Manage model risk documentation and validation processes suitable for regulatory review (e.g., RADV audits, Stars/HEDIS submissions) in partnership with compliance and quality teams.
• Own the cost, performance, and reliability of the ML platform footprint on Databricks, including compute optimization, cluster/job design, and vendor/tooling evaluation (e.g., build vs. buy decisions for platform capabilities).
• Collaborate with BI, data engineering, and data science teams to ensure ML features and pipelines align with the broader canonical data model and lakehouse architecture.
• Translate business problems from clinical, quality, finance, and operations stakeholders into well-scoped ML engineering initiatives with clear success metrics and delivery timelines.
• Communicate technical strategy, risk, and progress to senior leadership and non-technical stakeholders in clear, business-relevant terms.
• Perform any other job related duties as requested.

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field required
• Master's degree in Computer Science, Data Science, Engineering preferred
• Equivalent years of relevant work experience may be accepted in lieu of required education
• Eight (8) years in software/ML engineering required
• Five (5) years of leadership experience required
• Experience productionizing ML models at scale, including MLOps practices (CI/CD, model versioning, monitoring, retraining pipelines) required
• Experience operating within regulated, PHI-governed environments; working knowledge of HIPAA and healthcare data standards required

• Familiarity in a health plan, payer, or healthcare provider environment, with exposure to HEDIS/Stars, HCC risk adjustment, claims (837/835), and clinical data standards (HL7, FHIR, CCDA)
• Knowledgeable in cloud infrastructure (Azure preferred, given Databricks-on-Azure deployment) and Infrastructure-as-Code practices
• Hands-on proficiency with Databricks (or comparable lakehouse platform), Delta Lake, MLflow, and Spark; strong Python and SQL skills
• Solid understanding of ML fundamentals (supervised/unsupervised learning, model evaluation, feature engineering) and modern AI/LLM concepts (RAG, embeddings, prompt engineering, model evaluation for generative systems)
• Ability to evaluate or implement knowledge graph, entity resolution, or Member 360-style initiatives
• Strong communication skills with the ability to influence both technical teams and executive stakeholders
• Ability to present model risk or AI governance documentation to regulators, auditors, or compliance committees
• Strong service orientation and consulting skills
• Ability to work collaboratively with all levels of management
• Ability to juggle multiple complex priorities within a changing environment
• Strong leadership and management skills with the ability to motivate in a team-orientated, collaborative environment
• Strong knowledge of outsourcing and staff augmentation strategies supporting testing processes
• Knowledge of the managed care industry is preferred
• Exceptionally self-motivated and directed

• None

• General office environment; may be required to sit or stand for extended periods of time
• Ability to travel as required by the needs of the business.

Compensation Range:

CareSource takes into consideration a combination of a candidate’s education, training, and experience as well as the position’s scope and complexity, the discretion and latitude required for the role, and other external and internal data when establishing a salary level. In addition to base compensation, you may qualify for a bonus tied to company and individual performance. We are highly invested in every employee’s total well-being and offer a substantial and comprehensive total rewards package.

Compensation Type (hourly/salary):

Organization Level Competencies

• Fostering a Collaborative Workplace Culture

• Cultivate Partnerships

• Develop Self and Others

• Drive Execution

• Influence Others

• Pursue Personal Excellence

• Understand the Business

This job description is not all inclusive. CareSource reserves the right to amend this job description at any time. CareSource is an Equal Opportunity Employer. We are dedicated to fostering an environment of belonging that welcomes and supports individuals of all backgrounds.