Vice President, FinOps for AI

State Street

. Establish the enterprise FinOps for AI discipline, including scope, taxonomy, methodology, operating model, governance, controls, and performance measures .

Posted 9/29/2026full-timeQuincy • Massachusetts • United StatesLead💰 $110,000 - $188,750 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in establishing FinOps for AI, including governance, cost management, and optimization strategies. Proficient in budgeting, forecasting, and financial analysis related to AI investments and cloud spending.

Highest-signal resume keywords

FinOps ManagementCloud Financial ManagementAI EconomicsBudgeting and ForecastingCost Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Cloud Spend ManagementCost Per Token AnalysisVariance AnalysisFinancial ReportingModel Selection and RoutingCost EstimationAI Gateway ManagementToken-Based PricingAccelerated Compute ManagementData Platform Integration

Soft Skills

Effective PartnershipMatrixed Environment Collaboration

Tools & Technologies

Power BIMicrosoft FabricTableauCloud Financial Analytics Tools

Certifications & Qualifications

FinOps CertificationCloud Certification

Industry Keywords

AI Cost ManagementCloud Financial AnalyticsFinOps FrameworkFOCUS Cost Data StandardsTBM Taxonomy

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformTableau

About the role

Key responsibilities & impact

Establish the enterprise FinOps for AI discipline, including 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 unit economics such as cost per token, request, session, agent, and use case
Connect AI spending to products, services, business outcomes, and customer value
Lead budgeting, forecasting, scenario modeling, and variance analysis for AI investments
Establish early warnings when consumption or run rate exceeds plan
Set allocation, showback, and chargeback standards, including tagging, attribution, shared-cost treatment, and reporting
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
Embed cost estimation, financial guardrails, and onboarding guidance into AI intake and development workflows
Partner with AI and cloud platform teams, engineering, architecture, security, procurement, finance, and business stakeholders
Define reporting and tooling requirements and validate implementation logic
Communicate AI spend, risks, trade-offs, and opportunities through executive showback, leadership reviews, and planning cycles
Report to the Global Technology Services Cloud FinOps Operations Lead within the Technology Business Office

Requirements

What you’ll need

Bachelor’s degree in Finance, Accounting, Economics, Computer Science, Engineering, Information Systems, or a related field preferred
10+ years of equivalent relevant experience
Significant experience across FinOps, cloud financial management, technology finance, or cloud and AI engineering
Experience managing or optimizing cloud spend at scale across AWS, Azure, or GCP
Direct experience with AI or machine-learning economics, including model and inference costs, GPU or accelerated compute, AI gateways, token-based pricing, or AI software licensing strongly preferred
Experience with budgeting, forecasting, allocation, tagging, showback or chargeback, optimization, and cloud commitment management
Familiarity with the FinOps Framework, FOCUS cost data standards, or TBM taxonomy
Proficiency with cost and usage data and reporting platforms such as Power BI, Microsoft Fabric, Tableau, or comparable cloud financial analytics tools
Strong understanding of AI pricing and consumption drivers, including tokens, inference, context windows, model tiers, accelerated compute, licensing, and actual usage behavior
Limited travel may be required
Effective partnership across cloud and AI platform engineering, application teams, architecture, security, procurement, finance, and business leadership
Ability to work effectively in a matrixed environment
FinOps or relevant cloud certification preferred

Benefits

Comp & perks

401(k) retirement savings plan with company match
Basic life, medical, dental, and vision insurance
Long-term disability coverage
Optional additional insurance coverages
Paid vacation and sick leave
Short-term disability
Paid family care responsibilities leave
Employee Assistance Program
Annual performance-based incentive compensation
Tax-advantaged savings plans
Inclusive development opportunities
Flexible work-life support
Paid volunteer days
Employee networks