AI & FinOps Financial Analyst, Expert

Requisition # 174897

Job Category:
Accounting / Finance

Job Level: Individual Contributor

Business Unit:
Finance

Work Type:
Hybrid

Job Location:

Oakland

The Operations Finance team provides critical financial support to PG&E's core operating and enterprise functions, including financial analysis, budgeting, planning, forecasting, accounting support, operational performance reporting, strategic decision support, and value delivery. Operations Finance serves as a trusted advisor to business leaders by translating operational priorities into clear financial plans, identifying risks and opportunities, and helping the company deliver safe, reliable, affordable, innovative service to customers.

This role will support the Information Technology organization with a dedicated focus on artificial intelligence initiatives, including generative AI, agentic AI, AI-enabled automation, model consumption, cloud AI services, and emerging AI capabilities. The role will help PG&E apply financial rigor to AI investments by connecting technical usage patterns to measurable business value, ensuring savings are validated and captured, and building repeatable standards for AI business cases, cost governance, and value realization.

The Expert / Principal Operations Finance Analyst - AI Value Delivery & Fin Ops will serve as the finance lead for AI initiatives within PG&E's IT portfolio. This is not a project manager role. The successful candidate will be a strategic finance partner who understands both the AI landscape and the business case discipline required to convert AI activity into tangible financial outcomes.

The role will evaluate AI opportunities across the development lifecycle, determine whether value is expense reduction, capital deferral, productivity, risk reduction, customer experience improvement, or other measurable benefit, and ensure that benefits are modeled, validated, budget-owner-owned, and ultimately taken to the bottom line where applicable. The individual will partner closely with IT, AI product teams, architecture, cloud/Fin Ops, accounting, sourcing, business finance, and functional area leaders to build repeatable frameworks for AI investment decisions, model usage optimization, tokenomics, O&M tail forecasting, and capital-versus-expense treatment of AI-enabled products and agents.

This position is hybrid, working from the employee's remote office and our Oakland headquarters, based on business need.

• Lead finance support for AI business cases, ensuring costs, benefits, assumptions, risks, timing, dependencies, confidence levels, and value levers are clearly documented and decision ready.
• Translate AI use cases into financial outcomes, including O&M reduction, capital deferral, avoided work, productivity gains, quality improvements, risk mitigation, customer experience benefits, and other enterprise value levers.
• Partner with business owners to define how savings will be realized, who owns the benefit, when the benefit will hit forecast/budget, and what actions are required to convert productivity into bottom-line savings.
• Develop ROI, NPV, payback, sensitivity, and scenario analyses for AI initiatives across pilot, scale, and run-state phases.
• Establish benefit tracking routines that compare approved business case value to actual realized financial and operational outcomes.

• Build and maintain financial models for AI consumption, including token usage, model selection, inference cost, GPU or cloud AI service cost, prompt/context patterns, embeddings, vector database usage, orchestration costs, platform fees, licensing, support, and ongoing run costs.
• Partner with IT, cloud, enterprise architecture, and AI engineering teams to improve the cost effectiveness of AI workloads through model right-sizing, routing, caching, batching, prompt efficiency, capacity planning, vendor pricing evaluation, and usage guardrails.
• Create showback, chargeback, or allocation approaches that connect AI consumption to business owners, use cases, products, and outcomes.
• Analyze pricing sheets, vendor proposals, consumption trends, forecast variances, and unit economics to recommend financially optimal AI model and platform choices.
• Develop AI cost KPIs such as cost per task, cost per workflow, cost per case, cost per user, cost per avoided hour, cost per token, and cost per business outcome.