Staff FinOps Analyst

ServiceNow•Santa Clara, CA

23h•$176,100 - $308,200

About The Position

The Staff FinOps Analyst is a senior individual contributor in ServiceNow’s FinOps Governance team within Global Cloud Services. The role drives financial accountability and optimization across AI spending, applying AI infrastructure knowledge, data-driven governance, and cross-functional program leadership. It connects technical usage with financial discipline by working with engineering teams, cloud finance leadership, and cloud and LLM providers, including Azure, AWS, GCP, Anthropic, and OpenAI. The role operates independently and engages VP-level stakeholders to ensure AI investments are managed rigorously. It establishes cost and usage governance, identifies and addresses anomalies and optimization opportunities, implements spend guardrails, and communicates cost trends and forecasts through executive reporting.

Requirements

Experience leveraging or critically evaluating how to integrate AI into work processes, decision-making, or problem-solving.
8 years of related experience with a Bachelor's degree, 6 years with a Master's degree, 3 years with a PhD, 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 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 on AI cost management standards.

Responsibilities

Define, track, and systematically 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 surface 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; drive these opportunities 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, standards, and regular review cadence.
Prepare and deliver VP-level reporting and presentations on AI cost trends, optimization progress, and forecasts, translating technical data into clear financial narratives.
Own programs and workstreams independently by defining scope, managing stakeholders, and driving work to completion with minimal direction.