Vice President Insurance Data Analytics Jobs in Missouri

The Vice President of Data, Analytics & AI will lead our new Data, Analytics, and AI function, reporting directly to the COO. The VP, Data is responsible for defining and executing the enterprise data, analytics, and AI roadmap to enable connected and flexible data, client-facing digital products, and capabilities ranging from data governance and data management to architecture and engineering. This role provides executive leadership for data, analytics, and AI within the Data function and across NCM, ensuring data assets are scalable, secure, and aligned to business strategy. The VP will build and lead high-performing teams, modernize data platforms, govern data and AI responsibly, and deliver assets and digital products that generate measurable value for our business and our clients.

Duties and Responsibilities

Executive Leadership

• Define and execute the enterprise data, analytics, and AI roadmap and investment plans aligned to business strategy.
• Assess organizational readiness for data initiatives, including processes, tools, skills, and culture.
• Forecast data, infrastructure, and resource needs to support business demand and transformation initiatives.
• Partner with executive leaders to prioritize initiatives and ensure stakeholder alignment.

Data Governance, Risk & Compliance

• Develop, implement, and enforce enterprise data policies to ensure regulatory compliance, ethical use of data, and risk mitigation.
• Ensure ownership, management, and stewardship of data assets across the data lifecycle.

Digital Products & Value Delivery

• Own the lifecycle management of data, analytics, and AI products, from ideation and prioritization through delivery and ongoing enhancement to create and maintain high-quality, reusable data assets
• Define and link key performance indicators (KPIs) to digital products to measure business impact and drive continuous improvement.
• Plan the evolution of digital products with clear timelines, dependencies, and milestones to ensure reliability, performance, and user satisfaction.

• Foster a culture of data-driven decision-making across the organization through enablement and self-service analytics.

Artificial Intelligence & Machine Learning

• Lead the introduction of AI and machine learning solutions to automate processes and enhance business outcomes.
• Ensure responsible AI practices, including bias mitigation, explainability, and compliance with internal and external standards.

Data Architecture & Engineering

• Guide architecture and engineering to create and maintain standardized, connected, and flexible data structures across the enterprise.
• Standardize key data domains (e.g., customer, product, reference data), data integration patterns, and data storage solutions to ensure high-quality data, reusable data assets, seamless interoperability with internal and external systems, scalability, performance and cost optimization.

Data Operations, DevOps & Platform Modernization

• Enable collaborative, automated, and reliable data operations.
• Implement processes and technologies to accelerate delivery and reduce risk.
• Implement encryption, access management, and security controls for data at rest and in motion to ensure data privacy and compliance.

• Ensure applications are scalable, performant, and integrated with enterprise data platforms.

Talent Development & Enablement

• Build, lead, and mentor multidisciplinary teams across data governance, data management and stewardship, architecture, data engineering, analytics, AI, and platform operations.
• Deliver training and enablement programs to build data literacy and advanced skills across the organization.
• Establish career paths, performance standards, and a culture of innovation and continuous improvement.
  

Qualifications

• 15+ years of experience in data, analytics, and technology leadership, with significant experience at the enterprise level.
• Proven track record of delivering large-scale data, analytics, and AI initiatives that drive measurable business outcomes.
• Deep expertise in data governance, analytics, architecture (business, data, integration, and solution architecture), and modern data platforms.
• Strong understanding of regulatory, privacy, and ethical considerations related to data and AI.
• Demonstrated ability to influence executive stakeholders and translate business strategy into technical execution.
• Experience building and leading high-performing, cross-functional teams and leading those teams through change and ambiguity