Staff Product Data Scientist, ML Resource Efficiency

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

Job summary

Google Sunnyvale is hiring Share Staff Product Data Scientist, ML Resource Efficiency to optimize ML infrastructure and cost efficiency. You will analyze usage patterns, develop predictive models, and partner with Product, Finance and Engineering to drive data-informed decisions.

You will own end-to-end analyses, build robust data pipelines, and communicate insights to executives. A strong background in statistics, data science, and SQL is required, with excellent collaboration across teams.

Qualifications

• Bachelor's degree in a quantitative field (Statistics, Math, Data Science, Engineering, Physics, Economics)
• 10 years analytics experience with bachelor's or 8 years with a Master's, incl. coding in Python/R/SQL
• Experience articulating product questions and using statistics to inform decisions

Responsibilities

• Perform analysis using SQL, R, Python and provide analytical leadership
• Own outcomes from problem definition to metrics, data extraction, modeling and stakeholder presentation
• Develop solutions for ambiguous problems by framing hypotheses and recommendations
• Oversee cross-functional project timelines and drive process improvements
• Mentor colleagues and build capabilities in the specialization

Skills

Statistics

Data analysis

Python

SQL

R

Education

Bachelor's degree in quantitative field

Master's degree preferred

Tools

SQL

R

Python

SAS

Stata

MATLAB

Job description

Share Staff Product Data Scientist, ML Resource Efficiency

corporate_fare Google Sunnyvale, CA, USA

• 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.

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

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 and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy , Know your rights: workplace discrimination is illegal , Belonging at Google , and How we hire .

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.