AI FinOps & Token Economics Lead

Microsoft•Reno, NV

5d•$97,600 - $206,400

About The Position

The AI FinOps & Token Economics Lead will lead Microsoft Business Operations’ end-to-end approach to vendor AI consumption. The role ensures token investment is governed, forecast, allocated and optimized against measurable business outcomes. It will establish an operating model connecting vendor AI demand, consumption, commercial accountability and value realization, so investment is directed to the right AI capabilities and increased consumption contributes to lower cost, increased capacity, improved quality or better business outcomes. The position sits within Global Vendor Delivery & Solutions (GVDS), part of Microsoft Business Operations. GVDS develops and manages Microsoft’s global vendor delivery ecosystem and aims to build and run an AI-enabled vendor ecosystem that delivers stronger business outcomes, greater agility and structurally lower cost. The lead will develop demand and investment governance, maintain a consolidated view of vendor AI consumption, and connect token investments to measurable operational and financial outcomes. The role also incorporates token economics into vendor commercial governance, including statements of work, renewals and transformation discussions, while driving efficient model and usage choices. The position will evolve MBO’s operating model to treat AI as a form of capacity investment alongside vendor headcount.

Requirements

Bachelor’s degree in Finance, Business, Economics, Engineering or a related field and 4+ years of experience in FinOps, cloud or technology cost management, FP&A, or vendor/commercial management, or equivalent experience.
Strong commercial acumen and data-driven decision-making.
Ability to influence without authority and manage stakeholders and vendors confidently.
Structured problem-solving skills, a growth mindset and a customer-obsessed focus on business outcomes.
Ability to work across organizational boundaries as One Microsoft.

Nice To Haves

6+ years of experience in FinOps, technology cost management, FP&A or commercial management.
Hands-on cloud or AI consumption FinOps experience, such as Azure cost management or token- or usage-based pricing.
Understanding of large language models (LLMs), AI agents and token economics.
Experience with vendor or outsourcing commercial models, statements of work and renewals.
Strong forecasting and variance reporting skills using tools such as Power BI and Excel.
FinOps Certified Practitioner or similar certification.
Excellent executive communication and influencing skills.

Responsibilities

Own MBO’s framework for vendor token demand and consumption, including intake, demand forecasting and role- or workload-level requirements.
Define approval thresholds, the exceptions process, budget and funding accountability, and portfolio prioritization across vendor AI use cases.
Operationalize a per-vendor, per-month threshold, requiring exceptions to be justified against contracted outcomes and measurable business value.
Create a consolidated view of vendor AI consumption and establish a regular operating rhythm covering actual versus approved consumption, forecasts and run rates, variance and outliers, optimization opportunities, and consumption controls.
Align with the emerging internal AI FinOps model, focused on visibility, allocation, optimization and business outcomes rather than cost control alone.
Own FinOps and token economics for Copilot and Cowork, including usage visibility, allocation and optimization.
Link token investment to measurable outcomes through a value chain from token investment to AI capability, productivity or capacity, business KPIs and financial value.
Connect token investment to the broader vendor commercial model and determine where Microsoft or the supplier should bear AI consumption costs.
Ensure AI productivity is reflected in supplier capacity and commercial arrangements so token investment does not become incremental vendor cost.
Incorporate token economics into statements of work, renewals and vendor transformation discussions.
Apply the existing framework distinguishing Microsoft-directed requirements, supplier productivity choices and joint transformation investments.
Continuously optimize vendor AI consumption by using the lowest-cost capable tool or model, setting appropriate limits, identifying abnormal consumption and eliminating unnecessary usage.
Move workloads toward more efficient models and configurations, with particular focus on long-running agents, recurring automations, parallel sessions, large contexts and unnecessarily expensive models.
Recommend increased investment when the economics justify it.
Evolve MBO’s approach to treat AI as a capacity investment alongside vendor headcount, embedding token economics in planning, prioritization and vendor strategy.