Associate Vice President, Business Intelligence & Analytics Engineering

CareSource; United States; On-site; USD 180,000 - 280,000; Full time

Job summary

CareSource seeks a senior technology leader to advance its data analytics, data science, and AI capabilities across the enterprise. You will partner with clinical, financial, and operational units to translate complex data into actionable intelligence, driving improvements in member outcomes, Stars/HEDIS, risk stratification, and efficiency.

You will lead BI, Data Science, and AI/ML Engineering, setting standards, governance, and a scalable analytics platform.

Qualifications

• Bachelor's degree required in a quantitative field; Master's/PhD preferred.
• 10+ years of progressive experience in data analytics, data science, or AI/ML engineering.
• 4+ years in a senior leadership role managing technical teams.
• Experience in managed care/health plan environments preferred.
• Databricks experience (Unity Catalog, MLflow, Delta Live Tables) preferred.

Responsibilities

• Own the enterprise BI, analytics, and data product strategy and multi-year roadmap across the health plan.
• Partner with Finance, Clinical Ops, Stars Coordination, and others to define enterprise KPIs and self-service analytics.
• Establish enterprise data product standards, governance, and delivery frameworks.

Skills

Python

SQL

Spark/PySpark

Modeling

Leadership

Executive communication

Cross-functional collaboration

Education

Bachelor's degree in CS/Stats/BI

Master's degree or PhD in Data Science/Health Informatics

Tools

Databricks

Job description

The AVP, Business Intelligence & Analytics Engineering is a senior technology leader responsible for advancing CareSource's data analytics, data science, and artificial intelligence capabilities across the enterprise. This role partners directly with clinical, financial, and operational business units to translate complex data assets into actionable intelligence — driving measurable improvements in member outcomes, Stars & HEDIS performance, risk stratification, and operational efficiency. The AVP leads three integrated practice areas: Business Intelligence & Reporting, Data Science & Predictive Analytics, and AI/ML Engineering.

Essential Functions:

• Own the enterprise Business Intelligence, Analytics, and Data Products strategy, governance, and multi-year roadmap across the health plan, including claims, clinical, Stars/HEDIS, HCC risk, and Member 360 domains.
• Partner with Finance, Clinical Operations, Stars Coordination, Network Management, and other business leaders to define enterprise KPIs, performance metrics, and self-service analytics capabilities that support strategic decision-making.
• Establish enterprise data product standards, governance practices, and delivery frameworks that ensure scalable, trusted, and auditable analytics capabilities across the organization.
• Evaluate and manage analytics and BI technologies, including vendor platforms and Databricks-native capabilities, and lead build-versus-buy recommendations for executive leadership.
• Ensure all analytics products and reporting outputs comply with HIPAA, CMS, state regulatory, and enterprise governance requirements.
• Lead the enterprise data science function, driving predictive and prescriptive analytics solutions that improve clinical outcomes, operational performance, member experience, and financial results.
• Oversee the development, deployment, and ongoing optimization of advanced analytic models supporting risk adjustment, quality improvement, population health management, utilization management, care gap closure, fraud detection, and other strategic business priorities.
• Establish and operationalize an enterprise model lifecycle management framework, including feature management, experiment tracking, validation standards, model monitoring, and production deployment practices.
• Partner with Care Management, Quality, Provider Relations, Clinical Operations, and other business stakeholders to translate analytic insights into measurable operational and clinical improvements.
• Build, lead, and develop a high-performing organization of BI developers, data engineers, data scientists, ML engineers, statisticians, and analytics professionals, including workforce planning, succession management, career development, technical skill advancement, and organizational capability building.
• Own the strategy, architecture, governance, and evolution of the enterprise AI/ML platform, ensuring scalable, secure, and compliant deployment of advanced analytics and artificial intelligence capabilities.
• Lead the identification, prioritization, and execution of enterprise AI and generative AI use cases that improve business performance, enhance member and provider experiences, and increase organizational productivity.
• Establish and oversee responsible AI governance, including model risk management, fairness and explainability standards, bias monitoring, human oversight controls, AI ethics review processes, and audit readiness for regulatory and compliance reviews.
• Drive enterprise adoption of generative AI capabilities, including large language model (LLM) and retrieval-augmented generation (RAG) solutions, model evaluation frameworks, cost optimization strategies, and scalable deployment patterns.
• Collaborate with Information Security, Compliance, Legal, Privacy, and Technology leadership to ensure AI systems meet regulatory, security, auditability, and vendor risk management requirements.
• Develop reusable AI solution frameworks, accelerators, governance standards, and technical enablement resources that scale AI adoption across the enterprise.
• Serve as a key member of the Enterprise Data Services leadership team, contributing to enterprise data strategy, AI strategy, talent strategy, and the multi-year technology roadmap.
• Serve as the executive sponsor and primary advisor for enterprise analytics and AI initiatives, communicating strategy, performance, risks, opportunities, and business value to executive and Board-level stakeholders.
• Own financial planning and budget accountability for the analytics, data science, and AI engineering portfolio, including workforce investments, technology platforms, infrastructure, and strategic vendor partnerships.
• Foster a culture of innovation, continuous learning, experimentation, and responsible data- and AI-driven decision-making across the enterprise.
• Perform any other job related duties as requested.

Education and Experience:

• Bachelor's degree in Computer Science, Statistics, Mathematics, Biomedical Informatics, or a related quantitative field is required
• Master's degree or PhD in Data Science, Health Informatics, Applied Statistics, or equivalent preferred
• Equivalent years of relevant work experience may be accepted in lieu of required education
• Ten (10) years of progressive experience in data analytics, data science, or AI/ML engineering required
• Four (4) years in a senior leadership role managing technical teams is required
• Demonstrated experience in a managed care, health plan, or health insurance environment preferred
• Proven track record of delivering production AI/ML solutions in regulated, privacy-sensitive environments preferred
• Prior experience with Databricks (Unity Catalog, MLflow, Genie, Delta Live Tables) or equivalent modern lakehouse platforms strongly preferred
• Experience in vendor evaluation, contract negotiation, and technology partnership management preferred
• Experience with data governance frameworks including data cataloging, lineage, quality monitoring, and access control in a HIPAA-regulated context preferred

Competencies, Knowledge and Skills:

• Advanced proficiency in Python, SQL, and statistical programming languages (e.g., R, SAS), with experience leveraging Spark/PySpark and modern machine learning frameworks to develop and deploy scalable analytics and AI solutions
• Deep familiarity with claims, clinical, HEDIS/Stars, HCC, and member data models strongly preferred
• Deep understanding of AI/ML and MLOps practices, including model lifecycle management, model monitoring, CI/CD pipelines, experiment tracking, generative AI technologies, LLMs, prompt engineering, RAG architectures, and vector databases
• Strong strategic and business acumen, with the ability to align analytics, data science, and AI investments to organizational priorities, measurable outcomes, and mission impact
• Exceptional executive communication and influencing skills, including the ability to translate complex technical concepts into clear business value for executive leadership, clinical stakeholders, regulators, and non-technical audiences
• Proven leadership, talent development, and organizational management capabilities, with the ability to build, motivate, and lead high-performing teams in a collaborative and rapidly evolving environment
• Demonstrated ability to establish trusted partnerships and effectively collaborate across clinical, operational, technology, compliance, finance, and vendor organizations to achieve enterprise objectives
• Strong knowledge of healthcare and managed care data ecosystems, including HL7/FHIR standards, ICD-10/CPT coding, NCQA HEDIS specifications, CMS risk adjustment methodologies, and applicable regulatory requirements
• Highly self-directed with the ability to manage multiple complex priorities, drive execution through ambiguity, and effectively leverage vendor, outsourcing, and staff augmentation strategies to support organizational goals

Licensure and Certification:

• None

Working Conditions:

• 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.