Senior Product Data Scientist, ML Resource Efficiency

Google•Sunnyvale, CA

2d•$163,000 - $236,000

About The Position

Google’s bespoke ML TPU infrastructure is a rapidly growing investment driving performance beyond Moore’s Law. The Cloud ML Efficiency Data Science team provides insights and tools that help product areas efficiently consume ML resources for training and serving models. In this high-visibility role, you will collaborate with Capital Engineering, Finance, product managers, and executive leadership to support scalable, cost-effective deployment of ML compute across Google. You will use technical and analytical skills to identify efficiency opportunities through data transparency, software stack enhancements, user engagement, and service innovations such as pricing and product tiers. The role calls for a strategic, agile problem solver who can navigate ambiguity, act decisively, and build cross-functional relationships. You will partner closely with AI and Compute Enablement leads and regularly present findings to AI2 leadership. Your work will inform how Google optimizes investment and scales ML infrastructure globally to meet the demands of its ML products and research. You will engage with senior executives across Platforms, Research, Finance, and PA PARM teams to align services with user needs.

Requirements

Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
8 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL), or 5 years of such experience with a master's degree.

Nice To Haves

Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
8 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).
Experience working on Machine Learning Infrastructure.

Responsibilities

Perform analysis using relevant tools such as SQL, R, and Python; solve problems by narrowing multiple options to the best approach; and take ownership of open-ended, ambiguous business problems to reach optimal solutions.
Build processes, procedures, methods, tests, and components with foresight to anticipate and address future issues.
Report on Key Performance Indicators (KPIs) to support business reviews with cross-functional and organizational leadership teams; translate analysis results into business insights or product improvement opportunities.
Build and iteratively prototype analyses and business cases to provide insights at scale.
Develop comprehensive knowledge of Google data structures and metrics, and advocate for changes where needed for product development.
Influence across teams to align resources and direction.