Project Manager, AI Initiatives

Rockefeller Capital Management•Hamilton Township, NJ

About The Position

The Vice President, Project Manager, AI Initiatives will support the planning, execution, governance, and adoption of firmwide artificial intelligence initiatives across Rockefeller Capital Management. Reporting into the Technology Program Management Office, this role will partner closely with business, technology, information security, legal, compliance, risk, data, and vendor teams to move firmwide AI opportunities from intake and assessment through controlled delivery, launch, measurement, and ongoing governance. The ideal candidate combines strong project management discipline, practical familiarity with AI-enabled solutions, executive-level communication skills, and the ability to coordinate complex, cross-functional work in a regulated financial services environment.

Requirements

Bachelor’s degree in Technology, Business, Finance, or a related discipline; advanced degree preferred
7+ years of experience in technology program, project or product management within financial services or a similarly complex, regulated environment
Demonstrated experience managing enterprise-scale AI or Technology programs, including strategic vendor partnerships, platform launches, and cross-functional delivery
Strong working knowledge of AI/ML concepts, large language models, agentic AI architectures, and enterprise AI governance frameworks
Proficiency across the Copilot and Claude toolset — including Copilot Chat, Copilot Studio, Copilot Research, Cowork, Claude Skills, Claude Projects, Excel and Power Point plugins — to optimize research, reporting, project execution, and stakeholder communication.
Experience with wealth management or financial services technology ecosystems preferred, including familiarity with advisory platforms, data ecosystems, and client-facing digital products

Nice To Haves

Advanced degree preferred
Experience with wealth management or financial services technology ecosystems preferred, including familiarity with advisory platforms, data ecosystems, and client-facing digital products

Responsibilities

Manage the end-to-end delivery lifecycle for firmwide AI initiatives, including opportunity intake, prioritization, business case development, planning, execution, dependency management, launch readiness, firmwide roll-out, and adoption tracking.
Serve as a hands-on execution lead who can move between strategic planning and detailed follow-through, ensuring that AI initiatives maintain momentum while supporting Rockefeller’s fiduciary responsibilities, client trust, and operating discipline.
Collaborate with Technology, business stakeholders, and other control function partners to ensure AI initiatives are delivered within firm standards, regulatory expectations, and responsible AI governance requirements.
Maintain an enterprise view of the AI initiative portfolio, including status, milestones, risks, issues, dependencies, decisions, funding needs, vendor engagement, and executive-level reporting for leadership forums and governance routines.
Develop clear project plans, roadmaps, RAID logs, decision logs, action trackers, meeting materials, and executive updates that create transparency and drive accountability across multiple stakeholder groups.
Coordinate AI pilots, proofs of concept, agentic technology initiatives, workflow automation opportunities, and production deployments, ensuring that appropriate success criteria, controls, testing, adoption plans, and support models are defined before launch.
Support AI governance routines by preparing agendas, materials, readouts, follow-ups, and decision documentation, and by helping translate technical, operational, and risk considerations into practical business recommendations.
Partner with product, architecture, engineering, data, and vendor teams to clarify requirements, align delivery plans, manage dependencies, and resolve blockers across internal platforms and third-party AI solutions.
Participate in establishing and continuously improving repeatable AI delivery practices, including intake standards, prioritization criteria, implementation playbooks, control checkpoints, measurement frameworks, and operating cadences.
Drive business readiness and adoption planning for AI capabilities, including stakeholder communications, training coordination, change impact assessment, feedback loops, and post-launch performance monitoring.