Staff Product Data Scientist, ML Resource Efficiency

Google; Sunnyvale (CA); On-site; USD 192,000 - 278,000; Full time

Job summary

Google is seeking a senior data analytics professional to advance ML infrastructure and drive data-informed decision making across teams. You will work with Capital Engineering, Finance, Product Managers, and executive leadership to optimize scalable ML compute resources globally.

Responsibilities include leading analytical initiatives, building models, and presenting insights to senior stakeholders. The role emphasizes strategic thinking, ambiguity tolerance, and strong collaboration across

Qualifications

• Bachelor's degree in a quantitative field.
• 10 years of analytics experience solving product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) or 8 years with a Master's degree.

Responsibilities

• Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Provide analytical thought leadership through proactive and strategic contributions.
• Own outcomes for projects by defining problems, developing metrics, extracting/manipulating data, visualizing, implementing models, and presenting to stakeholders.
• Develop solutions, lead and manage ambiguous problems by framing, hypothesizing, and making recommendations combining analytical and product-specific expertise.
• Oversee integration of cross-functional project timelines, develop process improvements, and help define operational goals.
• Oversee contributions of others and develop colleagues’ capabilities in the area of specialization.

Skills

Analytical thinking

Communication

Problem solving

Education

Bachelor's degree in Statistics/Math/Data Science/Engineering/Physics/Economics or related field

Master's degree (preferred)

Tools

Python

R

SQL

BI tools

Databases

Data warehouses

Job description

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

Minimum qualifications:

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

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 13 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL), working with statistical packages (e.g. R, SAS, Stata, MATLAB, etc.), or 10 years of work experience with a Master's degree.
• Experience articulating product questions, pulling data from datasets (Python, R, SQL) and using statistics to arrive at an answer using analytical thinking and debugging skills.
• Experience with databases, data warehouses, and business intelligence and analytical tools.
• Knowledge of commercial reporting tools.

About The Job

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

Responsibilities

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.
• Oversee the contributions of others directly or indirectly, and develop colleagues’ capabilities in the area of specialization.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .