Minimum qualifications:
- Bachelor's degree in Business, Finance, Economics, Statistics, or another quantitative field, or equivalent practical experience.
- 7 years of experience in financial planning and analysis (FP&A), consulting, or a related function, or an advanced degree.
- Experience executing full-cycle FP&A functions including budgeting, forecasting, variance analysis, and modeling within a multinational company setting.
- Experience with programming (i.e. SQL).
Preferred qualifications:
- Experience with Cloud infrastructure or AI/ML hardware (e.g., GPUs, custom silicon).
- Experience streamlining cross-functional planning cycles and building models for capital-intensive infrastructure.
- Experience working with SQL, large datasets, and integrated financial systems.
- Experience partnering with and influencing cross-functional leadership across Engineering, Product, and Strategy.
- Experience managing free cash flow maximization and long-term priorities.
About the job:
As part of the Cloud AI Capacity planning function, this leadership position delivers critical business planning support and financial analytics to Google's Capacity Planning and PM teams. In this role, you will manage a portfolio to address intricate issues, providing the insights necessary for resource allocation. You will help business leaders navigate the complexities of the AI sector, ensuring an equilibrium between margin expansion and business growth.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $141000 - $206000 (USD) + 15% bonus target + equity + benefits
Learn more about
benefits at Google.
Responsibilities:
- Anticipate capacity constraints and implement efficiency gains that improve unit economics and overall Profit and Loss (P&L).
- Develop P&L and Margin models to articulate margin drivers to executive leadership.
- Develop Net Present Value (NPV) and cash flow models to evaluate profitability for future New Product Introduction (NPI) platforms and benchmark against engaged products.
- Analyze performance and Total Cost of Ownership (TCO) for AI Product Stock Keeping Units (SKUs) to incorporate insights into customer proposals.
- Partner with Cloud Product, Capacity, and Engineering on efficiency initiatives to utilize infrastructure and develop efficiency goals.
