Why This Role Stands Out
This hybrid leadership role offers significant career growth by empowering you to drive impactful AI and analytics strategies and build a data-driven culture. You'll thrive here if you're a strategic leader passionate about innovation and eager to shape the future of business intelligence. Apply today to make your mark!
Quick Overview
Job Description
Assistant Vice President of Business Analytics & AI
Summary Statement
Enterprise business leader responsible for execution and delivery of the enterprise data, analytics, and AI strategy in partnership with the CIDO and business leaders. Drives measurable business outcomes, builds data- and AI- culture, and governs data and AI for trusted reusable products. This role leads analytics products, data engineering, data governance, and enterprise AI execution including AI portfolio management and AI Operations to support profitable growth and operational efficiency. The AVP partners closely with business and technology leaders to embed trusted, value‑driven data, analytics and AI across the organization.
Essential Tasks/Major Duties
· Lead, direct, and develop staff in area of responsibility with an emphasis on talent management and succession planning in accordance with the company’s strategic direction.
· Execute the enterprise data, analytics, and AI strategy through roadmap delivery and portfolio management, including intake, prioritization, planning, value tracking, and benefits realization.
· Partner with business leaders to design, deliver, and embed analytics and AI solutions—including dashboards, models, and AI‑enabled decision support—driving adoption and measurable business outcomes.
· Lead analytics product management and AI factory, including lifecycle management, monitoring, drift detection, value tracking, and assessment of AI‑related operational, regulatory, and business risks in compliance with enterprise governance standards.
· Define and monitor KPIs for adoption, quality, timeliness, and business impact; present insights to senior leadership.
· Serve as the bridge between business and technology, ensuring successful delivery of data‑driven initiatives.
· Operationalize data governance, privacy, stewardship, and AI risk controls by partnering with Legal, Compliance, and Risk to ensure execution readiness, required reviews, and compliant documentation.
· Partner with Architecture and technology teams to ensure scalable data architecture, reliable integration and interoperability, security, and effective data lifecycle, retention, and recovery practices supporting continuity and regulatory needs.
· Guide investments in analytics talent, platforms, and technologies, and oversee vendor and system integrator performance, delivery outcomes, and value realization.
· Champion enterprise data and AI fluency, driving a data‑driven culture through training, communication, and engagement.
Knowledge, Skills & Abilities
· Bachelor’s degree in Data Science, Computer Science, Business, Statistics, or related field required; Master’s degree preferred.
· 15 years of progressive experience in data, analytics, AI, or technology‑related roles, with increasing responsibility and scope.
· 7 years of formal leadership experience, including leading cross‑functional teams and, preferably, managers or senior individual contributors.
· Demonstrated success delivering enterprise‑scale data, analytics, or AI initiatives that drive measurable business outcomes.
· Experience operating within P&C insurance, including collaboration with Risk, Compliance, Legal, and Audit stakeholders.
· Proven ability to partner effectively with business leaders and technology teams to translate strategy into execution, adoption, and realized value.
- Deep understanding of data architecture, data governance, advanced analytics (AI/ML), and analytics tools.
- Hands‑on familiarity with Databricks, AWS, Cliz, and integration to core P&C platforms (e.g., Guidewire).
- Experience operationalizing genAI/agentic AI in regulated environments (prompt engineering and evaluation, RAG/knowledge management, content moderation, cost & latency optimization).
- Demonstrated success implementing NAIC/NIST‑aligned AI governance and model risk management.
- Proven results shipping AI at scale in at least two of: underwriting workbench, claims assistant/triage, pricing experimentation platform, knowledge management for frontline staff preferred
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