(Remote) VP Enterprise Intelligence

Fidelity Life•Rosemont, IL

2d•$227,000 - $280,000•Remote

About The Position

Fidelity Life and eFinancial, part of iA Financial Group, are seeking a VP of Enterprise Intelligence to shape the intelligence agenda for US Life Insurance. Reporting to the CTO, US Life Insurance, this senior technology and business leader will lead the existing Data Engineering and AI/ML teams and establish a unified enterprise intelligence capability. The role is accountable for improving carrier reporting and analytics by creating trusted data foundations, decision-ready insights, and responsible AI capabilities that support business operations and growth. The VP will partner with executive leadership and teams across operations, distribution, marketing, product, finance, compliance, and technology to set priorities, reduce fragmentation, improve confidence in and access to data, and accelerate delivery of insights and intelligent solutions. The position will define and execute a multi-year enterprise data, reporting, analytics, and AI/ML strategy; strengthen data engineering and governance; modernize reporting; advance decision support; and build an integrated, high-performing organization. The role is remote, except that remote work is not available in California, Colorado, Connecticut, or New York. Compensation is $227,000–$280,000, with a comprehensive benefits package.

Requirements

Bachelor’s degree in computer science, data science, engineering, information systems, analytics, business, or a related discipline.
Significant progressive leadership experience in enterprise data, analytics, reporting, AI/ML, or related technology functions, including leadership of leaders and multidisciplinary teams.
Demonstrated success defining and executing enterprise data and analytics strategy in a complex, regulated organization.
Practical understanding of modern data architecture, cloud data platforms, integration, data products, governance, DataOps, MLOps, and the AI/ML lifecycle.
Executive presence and strong communication skills, including the ability to simplify complex topics and build alignment across diverse stakeholders.
Proven ability to lead transformation, influence without direct authority, resolve competing priorities, and sustain adoption through change.
Strong talent leadership, including the ability to attract, develop, engage, and retain specialized data and AI professionals.

Nice To Haves

Advanced degree.
Knowledge of life insurance business models, products, distribution, underwriting, policy administration, actuarial, claims, finance, and regulatory reporting.

Responsibilities

Own the vision, roadmap, operating model, and investment priorities for enterprise intelligence across US Life Insurance.
Establish a business-aligned portfolio of data products, analytics capabilities, reporting solutions, and AI/ML use cases with transparent value, ownership, and delivery commitments.
Partner with executives and functional leaders to identify critical decisions, define common metrics, and translate business needs into scalable intelligence capabilities.
Simplify and rationalize fragmented reports, dashboards, data pipelines, and analytical tools while preserving essential needs.
Create standards for enterprise metrics, semantic definitions, master and reference data, metadata, lineage, data quality, and accountable stewardship.
Ensure data and AI solutions meet expectations for security, privacy, compliance, resilience, model risk, auditability, and third-party oversight.
Guide modernization of data platforms and integration patterns, balancing speed, maintainability, cost, interoperability, and reuse.
Develop an enterprise reporting experience that helps leaders move from summary measures to actionable drivers and appropriately governed detail.
Define and communicate outcome-based measures for adoption, data quality, delivery speed, platform reliability, reporting efficiency, insight utilization, and business value.
Build data literacy and responsible AI fluency across the organization through communication, enablement, communities of practice, and hands-on partnership.
Unify and develop the Data Engineering and AI/ML teams around clear products, standards, accountabilities, career paths, and outcomes.
Build a disciplined AI/ML portfolio, including practical use cases, reusable capabilities, model lifecycle controls, and responsible adoption with business, risk, legal, and compliance stakeholders.