AI & FinOps Financial Analyst, Expert

Pacific Gas And Electric Company•Oakland, CA

8d•$122,000 - $194,000•Hybrid

About The Position

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 & FinOps 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/FinOps, 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.

Requirements

Bachelor’s degree in finance, Accounting, Economics, Business, Information Systems, Data Analytics, or 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 related field.
Demonstrated ability to build financial models, evaluate investment tradeoffs, develop business cases, and communicate financial recommendations to business leaders.
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.

Nice To Haves

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.

Responsibilities

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.
Partner with Accounting, Controllers, IT, and project teams 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 value realization evidence.
Ensure AI business cases are aligned 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 needed 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 costs, scale-up 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 business partners toward financially sound decisions that maximize ROI.