AI & FinOps Financial Analyst, Expert

PG&E Corporation is hiring an AI & FinOps Financial Analyst, Expert for a hybrid role based in Oakland, CA. In this position, you serve as the finance lead for AI initiatives across PG&E’s IT portfolio, connecting AI activity to measurable financial outcomes from pilot through scale and into run-state operations.

Role overview

The Expert / Principal Operations Finance Analyst – AI Value Delivery & FinOps translates AI business cases into decision-ready financial models. You will govern AI cost and usage economics, support accounting treatment, and help ensure value realization is tracked against approved business case assumptions.

Key responsibilities

• Lead finance support for AI business cases, documenting costs, benefits, assumptions, risks, timing, dependencies, confidence levels, and value levers for approval-ready decisions.
• Translate AI use cases into enterprise financial outcomes, including O&M reduction, capital deferral, avoided work, productivity and quality improvements, risk mitigation, customer experience benefits, and other value levers.
• Partner with business owners to define benefit ownership, the point at which savings hit forecast or budget, and the actions needed to convert productivity gains into bottom-line results.
• 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 AI consumption financial models covering token usage and model selection, inference cost, GPU or cloud AI service costs, prompt/context patterns, embeddings and vector database usage, orchestration costs, platform fees, licensing, and ongoing run costs.
• Work with IT, cloud, enterprise architecture, and AI engineering teams to improve cost effectiveness through model right-sizing, routing, caching, batching, prompt efficiency, capacity planning, vendor pricing evaluation, and usage guardrails.
• Create showback, chargeback, or allocation approaches that link 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/workflow/case/user, cost per avoided hour, cost per token, and cost per business outcome.
• Partner with Accounting and Controllers to evaluate capital-versus-expense treatment for AI-enabled products, agents, platforms, data work, implementation costs, software development, licensing, and ongoing support.
• Create repeatable standards, templates, decision trees, and governance routines for AI financial review, including intake requirements, cost categories, benefit taxonomy, accounting considerations, O&M tail assumptions, and evidence of value realization.
• Ensure AI business cases align with PG&E financial policies, utility accounting fundamentals, cost model requirements, regulatory considerations, and internal governance expectations.
• Support financial controls, auditability, data quality, cost attribution, and documentation required for defensible AI investment decisions.
• Identify and resolve gaps in AI cost transparency, ownership, tagging, allocation, and benefit accountability.
• Develop multi-year forecasts for AI initiatives, including pilot and scale costs, recurring O&M, licensing, cloud consumption, support resources, model retraining, monitoring, governance, and vendor services.
• Recommend optimized O&M tail assumptions based on actual and expected AI model usage patterns, adoption curves, unit cost trends, vendor pricing, and operational support needs.
• Support annual planning, monthly forecasting, variance analysis, and leadership reporting for AI-related IT spend and value delivery.
• Prepare executive-ready financial insights, decision papers, dashboards, and narratives that translate technical AI concepts into business implications for Finance and IT leadership.
• Challenge assumptions constructively, identify financial risks and opportunities, and influence partners toward financially sound decisions that maximize ROI.

Minimum qualifications

• Bachelor’s degree in finance, Accounting, Economics, Business, Information Systems, Data Analytics, or a related discipline, or equivalent work experience.
• 6 years of job-related experience in FP&A, business finance, technology finance, investment analysis, business case development, value delivery, FinOps, or a related field.
• Demonstrated ability to build financial models, evaluate investment tradeoffs, develop business cases, and communicate financial recommendations.
• Experience working with cross-functional stakeholders across Finance, Accounting, IT, product, engineering, sourcing, or operations.
• Strong understanding of planning, forecasting, budgeting, variance analysis, and financial governance.

Desired qualifications

• MBA, CPA, CFA, FinOps Certified Practitioner, cloud financial management certification, or equivalent experience.
• Experience supporting IT, cloud, AI, data, analytics, software, digital transformation, or enterprise technology portfolios.
• Working knowledge of generative AI, agentic AI, large language models, model usage patterns, token-based pricing, AI platform economics, and cloud AI services.
• Experience developing KPI frameworks, value realization models, benefit tracking routines, and executive dashboards.
• Understanding of utility accounting, regulatory finance, cost model concepts, capital-versus-expense treatment, and software capitalization principles.
• Ability to simplify complex technical concepts into clear financial narratives, influence decisions without direct authority, and operate in ambiguous, fast-evolving environments.

Compensation

• Estimated successful candidate placement: $134,000 - $150,000 (case-by-case).
• Bay Area minimum: $122,000; Bay Area maximum: $194,000.
• Eligible to participate in PG&E’s discretionary incentive compensation programs.

What success looks like

• AI initiatives have clear, decision-ready business cases connecting technical scope, model usage, implementation cost, run-rate O&M, accounting treatment, and measurable outcomes.
• Savings and productivity benefits are assigned to accountable business owners, incorporated into forecasts or budgets where appropriate, and tracked through realization.
• AI consumption is financially transparent through repeatable views of token usage, model selection, unit economics, cost drivers, vendor pricing, and optimized run-state recommendations.
• Accounting, Finance, IT, and business teams share a common framework for capital-versus-expense evaluation, O&M tails, and financial governance for AI agents, platforms, and enabled capabilities.
• Leadership can determine which AI investments create value, need course correction, or should be scaled, paused, redesigned, or retired based on evidence.
• PG&E applies repeatable AI finance standards that improve investment quality and reduce ambiguity around AI spend and enterprise value.