Staff Product Data Scientist, ML Resource Efficiency

We strive to understand our users, such that we can help enable an efficient and scalable ML infrastructure, by facilitating a deep understanding through rigorous analysis of opportunities to improve efficiency. These opportunities can be addressed via data transparency, software stack improvements, user engagements and service definition innovations (e.g., pricing, product tiers), which will have a lasting impact in the alignment of our service to our product area user needs. Your work will influence how Google spends, to cost-optimally scale and operate ML infrastructure that spans the world, and meets the rapidly growing needs of Google's ML products and research. You will work closely with many stakeholders, including senior executives in Capital Engineering, Finance, Platforms and Research, as well as product area resource management teams.

Google’s homegrown, bespoke ML TPU infrastructure is one of Google’s fastest growing infrastructure investments, which enables increases in performance despite the end of Moore’s Law. ML Efficiency Data Science is the team in Cloud that provides insights, tools and analyses that help ML infrastructure service consumers. To accomplish that, the data science team collaborates with teams cross-functionally such as Capital Engineering, Finance, Product Managers, PMO and executive leadership to enable the scalable, reliable, and efficient deployment and consumption of ML compute resources across Google.

In this role, you must be highly strategic, comfortable with ambiguity and be an exceptional communicator with a bias to action, as well as an agile and creative problem solver and able to build strong relationships and collaborate across functions.

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

US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

• Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Provide analytical thought leadership through proactive and strategic contributions (e.g., suggests new analyses, infrastructure or experiments to drive improvements in the business).

• Own outcomes for projects by covering problem definition, metrics development, data extraction and manipulation, visualization, creation, and implementation of analytical/statistical models, and presentation to stakeholders.

• Develop solutions, lead, and manage problems that may be ambiguous and lacking clear precedent by framing problems, generating hypotheses, and making recommendations from a perspective that combines both, analytical and product-specific expertise.

• Oversee the integration of cross-functional and cross-organizational project/process timelines, develop process improvements and recommendations, and help define operational goals and objectives.