Financial Analyst, Machine Learning, Corporate Finance

Socket.dev; Kirkland (WA); On-site; USD 116,000 - 166,000; Full time

Job summary

Google is seeking a Senior FP&A professional to guide corporate finance decisions for AI/ML investments. You will collaborate with cross-functional teams to provide forward-looking analysis, manage budgeting and forecasting, and deliver management reports that scale automated financial processes.

Individual pay is determined by experience, education, and performance, with a potential 15% bonus target, equity, and comprehensive benefits.

Qualifications

• Bachelor's degree in a quantitative field or equivalent practical experience.
• 4+ years in FP&A, consulting, or related function.
• Experience executing full-cycle FP&A: budgeting, forecasting, variance analysis, modeling.

Responsibilities

• Own governance of AI/ML compute portfolio data and reconcile numbers for long-range plan.
• Transform TPU-related data into visualizations, reports, and executive dashboards.
• Perform financial analysis to align investments with strategic priorities.
• Partner with Product Area Finance, TPMs, and central teams to drive allocation decisions.
• Execute due diligence, automated chargeback processes, and KPI monitoring.

Skills

FP&A experience

Full-cycle FP&A

Education

Bachelor's degree in Business, Finance, Economics, Statistics, or equivalent

Job description

Minimum qualifications:

• Bachelor's degree in Business, Finance, Economics, Statistics, a quantitative field, or equivalent practical experience.
• 4 years of experience in financial planning and analysis (FP&A), consulting, or a related function.
• Experience executing full-cycle FP&A functions, including budgeting, forecasting, variance analysis, and modeling.

Preferred qualifications:

• Experience building data infrastructure (e.g., designing architecture, building data pipelines, implementing scalable monitoring) and implementing reporting solutions.
• Experience developing strategies and making resource allocation decisions.
• Experience analyzing data to influence decision-making and generate business insights.
• Experience in project management and collaborating with cross-functional stakeholders.

About the job:

In this role, your focus will be on understanding where and how Google should invest to support the impact of our growing AI/Machine Learning investments. You will represent the Corporate Finance team and collaborate with cross-functional and finance stakeholders. This is a unique opportunity to have an outsized impact in a highly visible and strategic area of the company.

The name Google came from "googol," a mathematical term for the number 1 followed by 100 zeros. And nobody at Google loves big numbers like the Finance team when providing in depth analysis on all manner of strategic decisions across Google products. From developing forward-thinking analysis to generating management reports to scaling our automated financial processes, the Finance organization is an important partner and advisor to the business.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $116000 - $166000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Own the governance of AI/ML compute portfolio data, reconciling financial and capacity numbers to maintain a single source of truth on corporate long-range plan financials.
• Transform unstructured data from Tensor Processing Unit (TPU) resource requests and financial impacts into standardized data visualizations, comprehensive reports, and executive dashboards.
• Perform financial analysis to understand product area business and machine resource needs, ensuring investment decisions align with strategic priorities.
• Partner cross-functionally with Product Area Finance, Technical Program Managers (TPMs), and central teams to drive consensus on allocation decisions, short- and long-term planning, and shared AI/ML objectives.
• Execute due diligence, automated chargeback processes, and portfolio operating rhythms to monitor performance against key performance indicators (KPIs).