{"id":1255616,"url":"https://alion.io/job/google-staff-product-data-scientist-ml-resource-efficiency","title":"Staff Product Data Scientist, ML Resource Efficiency","company":{"id":82,"name":"Google","domain":"google.com","url":"https://alion.io/company/google","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":81,"open_postings":157,"ghost_share":0.045,"stale_share":0.611,"repost_share":0.045,"time_to_fill_p50_days":30,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":163000,"max_usd":322000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":465},"experience_years_min":13,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"TPU","optional":false},{"name":"MATLAB","optional":true},{"name":"SAS","optional":true},{"name":"Stata","optional":true}],"status":"live","first_seen_at":"2026-09-22T13:08:59Z","employer_posted_date":"2026-10-25","last_verified_at":"2026-10-29T03:16:49Z","board_verified":true,"closed_at":null,"days_open":36,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":36},"description":"About the job\nWe 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.\nGoogle’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.\nIndividual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits\nLearn more about benefits at Google.\nResponsibilities\nPerform 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).\nOwn 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.\nDevelop 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.\nOversee the integration of cross-functional and cross-organizational project/process timelines, develop process improvements and recommendations, and help define operational goals and objectives.\nOversee the contributions of others directly or indirectly, and develop colleagues’ capabilities in the area of specialization.\nQualifications\nMinimum qualifications:\nBachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.\n10 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.\nPreferred qualifications:\nMaster's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.\n13 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.\nExperience articulating product questions, pulling data from datasets (Python, R, SQL) and using statistics to arrive at an answer using analytical thinking and debugging 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