Director, Model Engineering & Operations

CareSource•Dayton, OH

8d•$135,600 - $237,400•Onsite

About The Position

The Director, Model Engineering & Operations is responsible for leading the strategy, development, implementation, and optimization of enterprise AI and machine learning solutions that support core health plan operations and business objectives. This role provides leadership for the design and delivery of scalable, secure, and compliant AI/ML platforms and production systems, while overseeing the end-to-end machine learning lifecycle. The Director leads a team of machine learning engineers and applied scientists and collaborates across business and technology functions to translate advanced analytics and artificial intelligence capabilities into reliable, cost-effective, and operationalized solutions that drive organizational performance and innovation.

Requirements

Bachelor's degree in Computer Science, Data Science, Engineering, or a related field required
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

Nice To Haves

Master's degree in Computer Science, Data Science, Engineering preferred
Equivalent years of relevant work experience may be accepted in lieu of required education
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

Responsibilities

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.