VP Data & AI/ML Engineering

You have a clear vision of where your career can go. And we have the leadership to help you get there. At CNA, we strive to create a culture in which people know they matter and are part of something important, ensuring the abilities of all employees are used to their fullest potential.
CNA seeks to offer a comprehensive and competitive benefits package to our employees that helps them - and their family members - achieve their physical, financial, emotional and social wellbeing goals.
For a detailed look at CNA's benefits, check out our Candidate Guide .
The Vice President of Data & AI leads the Data and AI/ML engineering teams and oversees Data Management, AI/ML Platform implementation, and Corporate and Finance systems at CNA. Reporting to the SVP of Tech Portfolio Execution, this role focuses on developing and executing strategies for data delivery, leading our ongoing data transformation to cloud, and improving data architecture and practices across the organization. The VP partners with Technology, Analytics, Data Science, and business leaders to build a comprehensive data vision and roadmap, positioning data as a key strategic asset. The role drives innovation, introduces new data technologies, and ensures alignment with business goals.
JOB DESCRIPTION:
Essential Duties & Responsibilities
Performs a combination of duties in accordance with departmental guidelines:

• Strategic Leadership: Develop and execute strategic and operational plans for Data & AI/ML engineering, implementation, management, and governance across CNA.
• Team Leadership: Lead, manage, coach, and develop a high-performing team, ensuring excellence in execution, talent management, and succession planning.
• Collaboration & Partnership: Work closely with the SVP of Enterprise Analytics & Data Management, and business stakeholders to establish delivery roadmaps aligned with business priorities and needs. Develop and manage strategic relationships with key vendors and partners in the data space.
• Problem Solving & Coordination: Lead cross-functional efforts to troubleshoot business challenges, identify root causes, and implement cost-effective solutions with technology partners and business users.
• Capability Assessment & Improvement: Evaluate current data and AI/ML capabilities, identify gaps, and prioritize improvements to enhance data-driven decision-making.
• Data Management Engineering: Lead and provide oversight for defining, designing, implementing, and supporting data assets in both legacy and cloud environments, ensuring seamless integration and optimization of data across platforms.
• Implementation of AI/ML Use Cases: Lead the engineering and execution of data and machine learning initiatives in partnership with the Analytics and AI leadership, that support business priorities and build organizational capability.
• Data Strategy Alignment: Work with Technology and business leaders to align data and AI/ML applications with the enterprise's overall strategic direction.
• AI/ML Platform Technology: Establishes strategic direction on the selection and integration of data platforms and technologies, ensuring they are fit-for-purpose, scalable, and maintainable.
• Financial and Corporate Systems: Oversees delivery and support for Reinsurance, HR and enterprise corporate applications for CNA, including Workday platform.
• Data Governance: Support the ongoing development and refinement of enterprise data governance policies in partnership with SVP of Enterprise Analytics & Data Management to ensure quality, security, compliance, and consistency across all data assets.
• Data & Analytics Operating Model: Document and implement the Data & Analytics Operating Model, working with business and Technology leaders to establish interim product management structures where necessary.
• Advocacy & Education: Act as the organization's champion for data management practices, driving awareness of data quality, governance, and stewardship. Foster collaboration and educate business leaders on the value of strong data practices.

May perform additional duties as assigned.
Reporting Relationship
Typically reports to SVP and above
Skills, Knowledge & Abilities

• Technical Expertise in Data & AI/ML: Deep understanding of data management, development, data structures, AI/ML platforms, and data science model implementation. Extensive knowledge of financial statistical reporting and data governance practices.
• People Leadership & Change Management: Proven ability to lead and develop teams with strong project management, listening, and change management skills.
• Industry Knowledge: Strong familiarity with the insurance industry, its products, services, and key financial performance metrics (preferred).
• Influence & Negotiation Skills: Ability to influence stakeholders across disciplines and negotiate effectively at senior levels with discretion and confidence.
• Cloud Data Migration & Modernization: Proven experience leading large-scale cloud data migration and modernization initiatives. Expertise with cloud data architectures and platforms required. Knowledge and experience with Google GCP preferred.
• Architecture Strategy and Target: Strong partnership with Enterprise Architecture in delivering a business outcome driven, incremental medallion NorthStar architecture while minimizing technical debt.
• Data Acquisition & Storage Strategy: Successful track record in developing and implementing data acquisition and storage strategies for large organizations.
• Data Analytics Understanding: Strong grasp of the purpose and value of data analytics in driving business insights and decision-making.
• Financial Reporting Expertise: In-depth knowledge of financial reporting requirements, general ledger processes, and accounting principles.
• Collaboration with Analytics Teams: Ability to work effectively with Analytics teams to execute data management, governance, AI/ML platform, and model implementation strategies.
• Statistical Reporting & Data Science Modeling: Significant experience with statistical reporting and developing data science models for business intelligence and forecasting.
• Business Software Proficiency: Proficient in Microsoft Office Suite and other relevant business software tools.

Education & Experience

• Bachelor's degree in Computer Science, Business Finance, Analytics, Information Technology, or a related field. A Master's degree is preferred, or equivalent experience.
• At least 12 years of experience in data management, development, and data science model implementation, with a minimum of 7 years in a management or leadership role.

CNA is committed to providing reasonable accommodations to qualified individuals with disabilities in the recruitment process. To request an accommodation, please contact [email protected] .