Senior FinOps Analyst - AI

About the role

We use AI across a growing portfolio of LLM-based products, and demand continues to increase. As that usage scales, understanding AI spend – where it is going, what is driving it, and which product areas are operating efficiently – has become an important discipline. We are building a team focused on bringing clarity, accountability, and actionable insight to that space.

This Senior Fin Ops Analyst role will be a key analytical position on that team. You will own the data and analysis behind our AI cost landscape, including extending cost and attribution models, building the pipelines that support them, producing insights that help teams understand the financial impact of their technical decisions, and identifying opportunities to optimize spend as adoption grows.

• Extend and operate our AI cost models – attributing spend at token, model, product, and team level across multiple LLM-based products and shared infrastructure
• Solve the hard attribution cases: shared endpoints, multi-tenant workloads, and internal platform services consumed by several products
• Define and track unit-cost metrics that make spend easier to interpret – cost per request, workflow, active user, or product – so teams can distinguish growth in usage from changes in efficiency
• Maintain the accuracy, lineage, and explainability of the underlying data so reported figures can be clearly supported

• Build and maintain Python pipelines that pull consumption telemetry from model provider APIs, AI platform services, and cloud billing, and normalize it into a single trusted dataset
• Automate recurring reporting and month-end close processes so manual effort decreases as usage and spend scale
• Establish anomaly detection with practical thresholds and clear escalation paths to identify unexpected cost increases before they become month-end surprises
• Own the BI layer in Power BI or Tableau, creating dashboards that are practical for engineering teams and credible for Finance stakeholders

• Investigate cost movements and produce clear, evidence-based explanations of what changed, why it changed, whether it is expected, and what it means going forward
• Identify and quantify optimization opportunities: model right-sizing, prompt and context efficiency, caching and batching, retrieval tuning, capacity commitments, redundant tooling
• Quantify cost, quality, and latency trade-offs across model options so product teams can evaluate alternatives using reliable data rather than intuition alone
• Track realized savings against a documented baseline
• Support AI spend forecasting and budget cycles with the underlying analysis and variance explanation

• Provide data and analysis that support governance decisions and enforcement, while policy decisions remain with leadership
• Maintain tagging and metadata standards that enable accurate attribution, and identify and resolve gaps that affect attribution quality
• Surface cost outliers and high-cost model choices early, framed as decision support for teams rather than as an approval gate

• Work directly with product and engineering teams so cost is considered during design rather than discovered after implementation
• Build cost literacy across consuming teams by helping them understand what drives spend and which levers they can control
• Contribute analysis and input to leadership reporting, and help shape how the team's methodology evolves
• Help define and, over time, mentor the second AI Fin Ops seat on the team

• 6–9 years of experience in Fin Ops, cloud cost management, technology finance, data analytics, or a closely related analytical role
• Strong…