Financial Analyst, Machine Learning, Corporate Finance

• Health, dental, vision, life, disability insurance
• Retirement Benefits: 401(k) with company match
• Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
• Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
• Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
• Baby Bonding Leave: 18 weeks
• Holidays: 13 paid days per year

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

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.

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).