FinOps AI Governance Lead

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, Service Now is the AI control tower for business reinvention. Our Service Now AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better.

We’re building an AI-native culture where technology and talent are unstoppable together. And we’re just getting started.

Join us to put AI to work for people.

Join Fin Ops Governance team that is part of the Global Cloud Services organization.

You’ll apply your expertise in AI cost management and data‑driven optimization to lead programs that bring accountability and control to Service Now’s AI investments. Working closely with engineering teams building AI‑powered skills and experiences, cloud finance leadership, and provider relationships with Azure, AWS, GCP, Anthropic and OpenAI, you’ll be the connective tissue between technical usage and financial discipline.

We are looking for a Staff Fin Ops AI Governance Lead to drive financial accountability and optimization across Service Now’s AI spend. This senior individual contributor role combines deep AI infrastructure literacy, data‑driven governance, and cross‑functional program leadership to ensure our AI investments are managed with the same rigor as our cloud infrastructure. The ideal candidate understands the economics of LLMs as well as they understand engineering and will thrive operating independently while engaging VP‑level stakeholders with confidence and clarity.

• Define, track, and systematically review AI cost and usage KPIs; identify anomalies and outliers that signal potential waste, misuse, or optimization opportunities.
• Design and operate an anomaly detection framework to surface suspiciously high AI usage across models and skill teams, and engage engineering collaboratively to investigate and remediate.
• Quantify, prioritize, and propose cost optimization opportunities—evaluating levers such as PTU vs. pay‑as‑you‑go trade‑offs, model tiering, context reduction, and caching—and drive them to measurable outcomes.
• Design preventive governance controls so that cost anomalies and overruns, once identified, are systematically prevented from recurring.
• Define and implement AI spend guardrails in coordination with cloud and LLM providers: set up budgets, configure alerts, manage commitment structures, and ensure contractual rate accuracy.
• Coordinate the AI Fin Ops governance program across engineering, finance, and cloud provider relationships—maintaining a clear operating model with documented standards and a regular review cadence.
• Prepare and deliver VP‑level reporting and presentations on AI cost trends, optimization progress, and forward‑looking forecasts, translating technical data into clear financial narratives.
• Operate as a self‑starter and self‑sufficient program owner: define scope, manage stakeholders, and drive work streams to completion with minimal direction.

• Strong working knowledge of LLM pricing models, multi‑cloud AI services (AWS Bedrock, Azure OpenAI, GCP Vertex AI), and AI cost optimization levers.
• Proficiency in data analysis using SQL and/or Python to build KPI frameworks, identify usage anomalies, and communicate findings through data visualization.
• Hands‑on experience with multi‑cloud cost governance across AWS, Azure, and GCP, including budget management, alerting, tagging, and billing API familiarity.
• Proven cross‑functional collaboration skills with engineering teams—ability to communicate cost impact, influence technical decisions, and drive efficiency improvements.
• Outstanding communication and executive presentation skills; comfortable preparing and delivering briefings to VP‑level audiences on complex technical and financial topics.
• Self‑directed and highly organized, with the ability to manage multiple concurrent work streams independently in a…