VP, Head of AI & Digital Experience
On-siteWhitehouse Station, New Jersey, United States
Job Summary
Prototype, deploy, and harden AI/ML and generative AI solutions for analytics, ML, and generative AI initiatives across three growth products. Define the AI strategy, set the roadmap, and own execution to tie every initiative to concrete business metrics. Ship working solutions in weeks while designing for extensibility, ensuring capabilities are reusable, governable, and transferable. Build and maintain a library of reusable AI skills for customer experience, digital, and marketing, coaching teams to adopt them safely. Automate ticket triage, draft support responses, and enable real-time agent copilots. Deploy on-site search, recommendation engines, and next-best-action prediction. Generate creative variants, segment audiences, forecast campaign performance, and optimize SEO and AEO content. Diagnose workflow and data infrastructure problems, partnering with Product, Engineering, and business leaders to translate ambiguous problems into scoped technical solutions. Establish governance, evaluation, and monitoring practices to keep all usage safe, responsible, and aligned with Chubb standards.
Required Qualifications
- Entrepreneurial mindset
- Ability to collaborate and build relationships across a large company
- Tenacity to find the shortest path to a working solution
- Comfort presenting a roadmap to senior leaders
- Ability to debug a data pipeline on a deadline
- Comfort building with what exists
- Ability to document what is learned
- Ability to raise the bar for teams
- Accountability for working across teams and with external partners
- Ownership of AI Development Life Cycle (AI-DLC) for 3 growth products
- Ability to own the extension and continuous improvement of the AI-DLC framework
- Ability to prototype, deploy, and harden AI/ML and generative AI solutions
- Ability to make real architectural calls
- Ability to unblock others
- Ability to ensure production systems are safe and maintainable
- Ability to define the AI strategy across analytics, ML, and generative AI
- Ability to tie every initiative to a concrete business metric
- Ability to distinguish between experiments that teach and investments that scale
- Ability to prioritize relentlessly
- Ability to ship working solutions in weeks
- Ability to design for extensibility across product lines
- Ability to avoid one-off builds
- Ability to ensure every capability shipped is reusable, governable, and transferable
- Ability to own a library of reusable AI skills
- Ability to prioritize which skills to build
- Ability to maintain quality and governance across AI skills
- Ability to coach teams to use AI skills well
- Ability to diagnose problems at the level of workflows, incentives, and data infrastructure
- Ability to design solutions that hold up when volume scales and team composition changes
- Ability to partner with Product, Engineering, Data, Operations, Risk, and business unit leaders
- Ability to translate ambiguous business problems into scoped technical solutions
- Ability to bring stakeholders along
- Ability to build and maintain governance, evaluation, and monitoring practices
- Ability to keep usage safe, responsible, auditable, and aligned with Chubb standards
- Ability to operate within and strengthen the existing AI-DLC framework
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