[Hiring] Director Product Management - AI @Sedgwick

Role Description

The Director, Product Management – AI Enabled Enterprise Data Products is responsible for defining and executing the product strategy, roadmap, and business outcomes for Sedgwick’s enterprise AI-enabled data product portfolio.

• Owns a portfolio of strategic data products including:
  • Enterprise AI Analyst and conversational analytics platforms that allow users to ask natural language questions and receive trusted insights, visualizations, recommendations, and analysis.
  • Enterprise data sharing, data exchange, marketplace, semantic layer, data catalog, and self-service analytics capabilities.
  • AI-enabled operational decision support products and analytical applications.
• Serves as the bridge between business strategy, data strategy, AI innovation, technology execution, governance, and client value realization.
• Leads Product Managers and Product Owners while partnering with Data Engineering, Architecture, Security, Governance, AI Engineering, Analytics, and Business leaders.

Qualifications

• Bachelor's degree in Business, Computer Science, Information Systems, Engineering, Data Science, or related field required.
• Master's degree preferred.
• 10+ years of progressive experience in Product Management, Data Products, Analytics Platforms, AI Products, or Enterprise Technology.
• 5+ years leading Product Managers or Product Owners and managing product portfolios.
• Experience owning enterprise-scale data, analytics, AI, platform, reporting, self-service analytics, semantic modeling, AI/ML, or conversational AI products.
• Experience defining product strategy, roadmaps, business cases, and measurable outcomes for data-driven products.
• Experience working in Agile or scaled Agile environments and leading cross-functional teams across Architecture, Data Engineering, Analytics, Security, Governance, and Business stakeholders.

Requirements

• Define and own the multi-year vision, strategy, roadmap, business model, and investment priorities for enterprise AI and data products.
• Establish a product portfolio that enables business users, clients, and operational teams to discover, access, share, and analyze trusted enterprise data.
• Drive AI-powered analytical experiences that allow users to interact with enterprise data through natural language and conversational interfaces.
• Develop business cases, success measures, portfolio-level OKRs, and executive communications on progress, risks, and architectural implications.
• Oversee delivery across multiple teams, ensuring alignment between product strategy, technical execution, architectural direction, and client outcomes.
• Set expectations for backlog quality, acceptance criteria, prioritization, capacity planning, dependency management, and delivery risk mitigation.
• Own product portfolio KPIs and contributions to Product Group and enterprise OKRs, including quality, efficiency, sustainability, and value delivery.
• Ensure solutions comply with enterprise security, privacy, regulatory, governance, and responsible AI requirements.
• Establish product management practices for enterprise data products, including data-as-a-product principles.
• Define and manage reusable, governed data products supporting claims, finance, compliance, operations, client, and executive reporting needs.
• Drive adoption of semantic models, business glossaries, metadata, cataloging, and self-service analytics capabilities.
• Lead product strategy for enterprise data-sharing solutions for internal consumers, clients, partners, and third-party integrations.
• Ensure data products are reliable, scalable, discoverable, secure, usable, and supported by lineage and governance.
• Own the product vision for AI-powered analytics that provide trusted answers, insights, metrics, visualizations, and recommendations through natural language interactions.
• Partner with business teams to identify high-value analytical use cases and decision-support opportunities.
• Define requirements for semantic layers, knowledge models, retrieval experiences, governance controls, trust mechanisms, and user experiences.
• Measure AI product effectiveness through adoption, accuracy, user satisfaction, business impact, and operational efficiency.
• Define strategy and roadmap for enterprise-scale data exchange and sharing capabilities.
• Build strong relationships with business leaders, claims operations, finance, client services, technology leaders, and external stakeholders.
• Lead and develop Product Owners, Scrum Masters, and product delivery teams.

Benefits

• Work-life balance.
• Caring culture.
• Opportunities for career growth.