Director, Medical Economics

Molina Healthcare•Long Beach, CA

About The Position

Leads the design, development, and standardization of healthcare data assets supporting Medical Economics, Finance, Actuarial, and operational business functions. This role is responsible for driving scalable data frameworks, governance practices, and cross-functional alignment to improve the consistency, usability, and strategic value of enterprise data. Initial priorities for this role include development of enterprise service category models and enhancement of key healthcare datasets supporting cost of care analysis. The ideal candidate combines healthcare data expertise, strategic thinking, and strong cross-functional leadership skills with the ability to translate complex business needs into scalable data solutions.

Requirements

At least 8 years of health care analytics and/or medical economics experience, or equivalent combination of relevant education and experience.
At least 3 years management/leadership experience.
Bachelor’s degree in statistics, mathematics, economics, computer science, health care management or related field.
Advanced understanding of Medicaid and Medicare programs or other health care plans.
Advanced analytical work experience within the health care industry (i.e., hospital, network, ancillary, medical facility, health care vendor, commercial health insurance, large physician practice, managed care organization, etc.)
Advanced proficiency with retrieving specified information from data sources.
Advanced knowledge of health care operations (utilization management, disease management, HEDIS quality measures, claims processing, etc.)
Advanced knowledge of health care financial terms (e.g., PMPM, revenue) and different standard code systems (ICD-10CM, CPT, HCPCS, NDC, etc.) utilized in medical coding/billing (UB04/1500 form).
Advanced understanding of key managed care concepts and provider reimbursement principles such as risk adjustment, capitation, FFS (Fee-for-Service), Diagnosis Related Groups (DRG’s), Ambulatory Patient Groups (APG’s), Ambulatory Payment Classifications (APC’s), and other payment mechanisms.
Advanced understanding of value-based risk arrangements
Advanced experience in quantifying, measuring, and analyzing financial, operational, and/or utilization metrics in health care.
Advanced problem-solving skills.
Advanced critical-thinking and attention to detail.
Ability to effectively collaborate with technical and non-technical stakeholders, and engage with various levels within the organization.
Strong time-management skills, and ability to manage simultaneous projects and tasks to meet internal deadlines.
Strong verbal and written communication skills.

Nice To Haves

Experience supporting Medical Economics, Actuarial, Finance, or Value-Based Care functions.
Experience developing classification models, taxonomies, or standardized healthcare data frameworks.

Responsibilities

Lead the design and governance of enterprise healthcare data assets and frameworks.
Establish scalable data structures, taxonomies, and business rules supporting reporting, analytics, financial management, and operational decision-making.
Drive development of enterprise service category models and related classification frameworks.
Partner with business and technical teams to evolve data assets that support current and future organizational needs.
Define standardized methodologies, hierarchies, and definitions to improve enterprise consistency and data integrity.
Collaborate with technical teams to improve data quality, stewardship, and scalability of enterprise datasets.
Partner closely with Medical Economics, Actuarial, Finance, Clinical Operations, and IT leadership to align data initiatives with business priorities.
Facilitate governance discussions and drive consensus on enterprise data standards and priorities.
Translate complex business requirements into actionable data and operational strategies.
Lead and mentor analysts and data stewards supporting enterprise data initiatives.
Establish clear priorities, governance processes, and delivery expectations across multiple concurrent initiatives.
Promote operational discipline, documentation standards, and sustainable support models.
Foster collaboration and accountability across teams and stakeholders.
Support data governance efforts related to data quality, stewardship, change management, and adoption.
Promote effective use and understanding of datasets across business areas.
Identify opportunities to improve data accessibility, consistency, and operational efficiency.