Staff FinOps Analyst

ServiceNow•Santa Clara, CA

About The Position

The Staff FinOps Analyst is a senior individual contributor responsible for financial accountability and optimization across ServiceNow’s AI spend. The role combines AI infrastructure expertise, data-driven governance, and cross-functional program leadership to ensure AI investments are managed with rigor comparable to cloud infrastructure. The analyst is expected to understand LLM economics and engineering, operate independently, and communicate confidently with VP-level stakeholders. The position is part of the FinOps Governance team within Global Cloud Services. It works closely with engineering teams building AI-powered skills and experiences, cloud finance leadership, and providers including Azure, AWS, GCP, Anthropic, and OpenAI. The role coordinates the AI FinOps governance program across engineering, finance, and provider relationships, and translates technical usage data into financial insights, optimization outcomes, and executive reporting.

Requirements

Experience leveraging or critically evaluating how to integrate AI into work processes, decision-making, or problem-solving, such as using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact.
Eight years of related experience with a bachelor's degree; or six years with a master's degree; or a PhD with three years of experience; or equivalent experience.
Strong working knowledge of LLM pricing models, multi-cloud AI services (AWS Bedrock, Azure OpenAI, and 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 familiarity with billing APIs.
Proven ability to collaborate across functions with engineering teams, communicate cost impact, influence technical decisions, and drive efficiency improvements.
Outstanding communication and executive presentation skills, including preparing and delivering briefings to VP-level audiences on complex technical and financial topics.
Self-directed and highly organized, with the ability to independently manage multiple concurrent workstreams in a fast-paced environment.
Bachelor’s degree in a quantitative field such as Computer Science, Engineering, Finance, Mathematics, or a related field.

Nice To Haves

Experience with AI optimization techniques such as model routing strategies, prompt engineering, context length management, batch inference, and caching.
Familiarity with provider-level budget and commitment tools, including Azure Cost Management, AWS Budgets, GCP Billing controls, and provider credit management.
Understanding of agentic AI cost patterns, including per-agent token attribution, multi-agent cost multiplication, and RAG workflow cost implications.
Experience with GenAI gateway or LLM proxy platforms for token metering, rate limiting, and cost attribution.
Prior involvement with FinOps Foundation working groups or industry communities focused on AI cost management standards.

Responsibilities

Define, track, and regularly review AI cost and usage KPIs; identify anomalies and outliers that may indicate waste, misuse, or optimization opportunities.
Design and operate an anomaly detection framework to identify unusually high AI usage across models and skill teams, and collaborate with engineering to investigate and remediate issues.
Quantify, prioritize, and propose cost optimization opportunities, including PTU versus pay-as-you-go trade-offs, model tiering, context reduction, and caching, and drive them to measurable outcomes.
Design preventive governance controls to prevent identified cost anomalies and overruns from recurring.
Define and implement AI spend guardrails with cloud and LLM providers, including budgets, alerts, commitment structures, and contractual rate accuracy.
Coordinate the AI FinOps governance program across engineering, finance, and cloud provider relationships; maintain a documented operating model and regular review cadence.
Prepare and deliver VP-level reports and presentations on AI cost trends, optimization progress, and forecasts, translating technical data into clear financial narratives.
Own programs independently by defining scope, managing stakeholders, and driving workstreams to completion with minimal direction.