Director of Data, Analytics, and AI

MEM•St. Louis, MO

1d•$144,000 - $216,000

About The Position

MEM Insurance is seeking a visionary Director of Data, Analytics & AI to lead their enterprise data strategy, analytics capabilities, and AI/ML practice. This leadership role involves partnering with executive and operational leaders to transform business priorities into data-driven solutions. The Director will establish the strategy, governance, platforms, and practices for trusted decision-making, oversee scalable data foundations, advanced analytics, and responsible AI initiatives, ensuring compliance within a regulated insurance environment. This position offers the opportunity to influence enterprise strategy, drive innovation, build high-performing teams, and define the future of MEM Insurance through data and AI.

Requirements

Bachelor's degree in Computer Science, Data Science, Information Management, or a closely related field required.
Equivalent combinations of education and relevant professional experience will be considered.
Valid driver's license
Proven track record of successful leadership in data, analytics, and artificial intelligence initiatives.
3+ years of leadership experience, either through formal management responsibilities or significant leadership influence within complex initiatives.

Nice To Haves

Experience developing and executing enterprise data strategies and governance programs preferred.
Experience leading cross-functional teams and driving business transformation through data and analytics strongly preferred.
Experience operating within a regulated industry environment preferred.

Responsibilities

Own and continuously evolve MEM's enterprise data, analytics, and AI strategy to align with business priorities, organizational goals, and risk management requirements.
Partner with Product Management and business leaders to understand strategic objectives and translate them into actionable data, analytics, and AI roadmaps.
Establish the long-term vision for data platforms, analytics environments, and AI/ML infrastructure, including build, buy, and partner decisions.
Identify and evaluate emerging AI capabilities and prioritize investments based on business value, organizational readiness, and risk-adjusted returns.
Maintain executive-level engagement through transparent communication of portfolio outcomes, delivery progress, and risk considerations.
Lead the organization's AI/ML practice, ensuring solutions are reliable, scalable, and deliver measurable business outcomes.
Establish enterprise standards for model development, validation, deployment, monitoring, and retirement.
Guide the evaluation, piloting, and deployment of generative AI and emerging AI technologies while balancing innovation with responsible governance.
Promote enterprise adoption of AI-enabled tools and opportunities that improve effectiveness, efficiency, and decision-making.
Define and oversee operational practices for machine learning including model management, monitoring, observability, and lifecycle governance.
Direct enterprise data architecture and data platform strategies that support analytics, AI, and operational business needs.
Ensure data engineering practices deliver high-quality, trusted, well-documented, and scalable data assets.
Partner with technology and operational leaders to maintain a unified data ecosystem that supports future growth and innovation.
Champion user-centered design with a strong focus on the usability and accessibility of analytics and data products.
Lead enterprise data governance efforts, including data quality, lineage, stewardship, access controls, retention practices, and business glossaries.
Oversee the AI governance framework including model inventories, risk classification, validation standards, monitoring, explainability, and fairness assessments.
Ensure data and AI initiatives comply with applicable insurance regulations, emerging AI requirements, and internal governance standards.
Partner with Legal, Compliance, Risk, and other stakeholders to translate regulatory expectations into practical operational controls.
Identify and proactively manage technical, operational, regulatory, and reputational risks associated with data and AI initiatives.
Partner with leaders across underwriting, claims, actuarial, safety and risk services, and other business functions to deliver analytics and AI solutions that improve business outcomes.
Foster strong cross-functional relationships to ensure alignment between business priorities and technology investments.
Drive measurable value through data-informed decision-making and enterprise analytics initiatives.
Lead, mentor, and develop a multidisciplinary team including product owners, data engineers, data scientists, analytics engineers, and business intelligence developers.
Create career development pathways, succession plans, and talent strategies that strengthen organizational capabilities.
Build a culture of innovation, accountability, collaboration, and continuous learning.
Develop future technical leaders through coaching, mentorship, and challenging growth opportunities.