Senior FinOps Analyst — AI

RELX•Raleigh, NC

About The Position

This role is for a Senior FinOps Analyst focused on AI costs within LexisNexis Legal & Professional. The analyst will be responsible for measuring, analyzing, and optimizing AI costs across the organization. This involves owning data, analytics, and reporting to help teams understand the financial impact of AI adoption and building scalable solutions for decision-making and sustainable growth. The role requires close partnership with engineering, product, and finance stakeholders to enhance visibility, forecasting, and cost efficiency. The operating model emphasizes enablement, making cost implications visible early and clearly to inform decisions, influencing through data and insights rather than gatekeeping. LexisNexis Legal & Professional is part of RELX, a global provider of information-based analytics and decision tools. The company is a leader in applying AI and advanced technologies to the legal market, developing ethical generative AI solutions with a flexible, multi-model approach. It employs over 2,000 technologists, data scientists, and experts who develop, test, and validate solutions in line with RELX Responsible AI Principles.

Requirements

Experience in FinOps, cloud cost management, technology finance, data analytics, or a related analytical role.
Strong analytical judgment, attention to detail, and ability to explain methodology, assumptions, and results.
Working knowledge of AI inference cost drivers, including tokens, context length, model tier, caching, retrieval patterns, and agentic workload behavior.
Python experience building APIs, data pipelines, normalized datasets, and repeatable automation.
Strong SQL skills and dashboard-building experience in Power BI or Tableau for technical and finance audiences.
Comfort using AI-assisted development tools such as Codex or Claude to accelerate analysis, automation, documentation, or pipeline work.
Automation-first mindset focused on scalable reporting and reduced manual effort.
Clear written communication that turns complex cost data into concise, actionable narratives.

Responsibilities

Own AI cost modeling and attribution across tokens, models, products, teams, and shared infrastructure.
Define unit-cost metrics that distinguish usage growth from efficiency changes.
Maintain accurate, explainable cost data with clear lineage and defensible methodology.
Build Python pipelines that normalize telemetry, provider API data, platform usage, and cloud billing into trusted datasets.
Automate recurring reporting, month-end close support, and anomaly detection.
Develop practical BI dashboards for engineering teams and credible reporting for Finance stakeholders.
Analyze cost movements and explain drivers, expected impacts, and forward-looking implications.
Identify, quantify, and track optimization opportunities across model selection, prompt efficiency, caching, batching, retrieval, commitments, and tooling overlap.