Director of Data, Analytics, and AI

Posted Thursday, August 20, 2026 at 6:00 AM

Are you passionate about shaping the future of data, analytics, and artificial intelligence in a business-critical environment? MEM Insurance is seeking a visionary Director of Data, Analytics & AI to lead our enterprise data strategy, analytics capabilities, and AI/ML practice. In this highly visible leadership role, you will partner with executive and operational leaders across the organization to transform business priorities into data-driven solutions that create measurable impact.

As the leader of MEM's data and AI function, you will establish the strategy, governance, platforms, and practices that enable trusted decision-making across the enterprise. You will oversee the development of scalable data foundations, advanced analytics capabilities, and responsible AI initiatives while ensuring compliance within a regulated insurance environment. This role offers the opportunity to influence enterprise strategy, drive innovation, build high-performing teams, and help define how data and AI will shape the future of MEM Insurance.

• 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.

• 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…