FinOps AI, Vice President

The Vice President, Fin Ops for AI will establish and lead the enterprise Fin Ops discipline for artificial intelligence. The role will provide financial governance, cost transparency, forecasting, allocation, optimization, and reporting across generative AI platforms, foundation models, AI gateways, agentic solutions, copilot technologies, accelerated compute, and embedded AI services. Acting as the strategic link among Technology, Engineering, Finance, Procurement, and business leadership, this leader will help ensure that AI innovation delivers measurable value with clear financial accountability.

Who We Are Looking For Reporting to the Global Technology Services Cloud Fin Ops Operations Lead within the Technology Business Office, the candidate will define how Street governs, measures, forecasts, allocates, and optimizes AI spend across the enterprise.

This is a hands-on practitioner-leader role. The successful candidate will build the methodology, analyze cost and usage data, partner with engineering and application teams at the point of consumption, and present a clear view of AI economics to senior leadership. The role requires sound judgment and adaptability as taxonomies, tooling, ownership models, and vendor pricing continue to evolve.

Why This Role Is Important To UsAI adoption and spending are growing rapidly and span cloud services, model providers, gateways, data platforms, accelerated compute, and software licensing. Because these services use pricing models that differ from traditional infrastructure, disciplined financial governance is essential to maintain transparency, manage risk, and guide investment decisions.

This role will create clear accountability for AI costs, establish unit economics before consumption patterns become entrenched, and ensure new workloads are estimated and sized before development begins. The work will support optimization targets and accelerate the shift from reactive reporting to cost-aware engineering and investment management.

What You Will Be Responsible For Establish the enterprise Fin Ops for AI discipline, including its scope, taxonomy, methodology, operating model, governance, controls, and performance measures.

Create an integrated view of AI cost and consumption across hyperscaler services, accelerated compute, AI gateways, models, tokens, agents, copilots, data platforms, and embedded vendor capabilities.

Define actionable unit economics—including cost per token, request, session, agent, and use case—and connect AI spending to products, services, business outcomes, and customer value.

Lead budgeting, forecasting, scenario modeling, and variance analysis for AI investments, with early warning when consumption or run rate moves ahead of plan.

Set allocation, showback, and chargeback standards, including tagging, attribution, shared-cost treatment, and reporting by business unit, application, product, and use case.

Identify and deliver optimization opportunities through model selection and routing, prompt and context efficiency, caching, batching, inference right-sizing, accelerated-compute commitments, and retirement of low-value consumption.

Track and validate realized savings and cost avoidance, clearly distinguishing actual results from estimates, forecasts, and recommendations.

Embed cost estimation, financial guardrails, and onboarding guidance into AI intake and development workflows so teams understand expected run costs and trade-offs before they build.

Partner with AI and cloud platform teams, engineering, architecture, security, procurement, finance, and business stakeholders to secure reliable data, influence consumption decisions, and support effective governance.

Define reporting and tooling requirements, validate implementation logic, and communicate AI spend, risks, trade-offs, and opportunities through executive showback, leadership reviews, and planning cycles.

What We Value These skills will help you succeed in this role:

Practical Fin Ops or cloud financial management experience, with the credibility to influence engineering and business decisions tied to real consumption.

Strong understanding of AI pricing and consumption drivers, including tokens, inference, context windows, model tiers, accelerated compute, licensing, and actual usage behavior.

Ability to build a new operating discipline from the ground up by setting methodology, making defensible assumptions, documenting decisions, and adapting as the market matures.

Strong analytical judgment and a…