Senior Product Data Scientist, ML Resource Efficiency

Socket.dev; Sunnyvale (CA); On-site; USD 163,000 - 236,000; Full time

Job summary

Google’s Cloud ML Efficiency Data Science team seeks a senior data scientist to optimize ML compute deployment. You will collaborate with finance, PMs, and executives to drive cost-effective scaling of ML resources worldwide.

Ideal candidates will have strong analytics, coding in Python/R/SQL, and experience with ML infrastructure. The role requires strategic thinking, ambiguity tolerance, and stakeholder management.

Qualifications

• Bachelor's degree in quantitative field required; master's preferred.
• 8 years analytics experience or 5 years with a Master’s degree.
• Proficiency in Python, R, SQL and statistical methods.

Responsibilities

• Analyze data to solve product and business problems.
• Develop scalable analytics processes and tests.
• Report KPIs to leadership and translate results to insights.
• Prototype analyses and build business cases for scale.
• Influence cross-functional teams on resources and direction.

Skills

Analytics experience

Statistical analysis

Programming

Python

R

SQL

Data-driven

Education

Bachelor's degree in a quantitative field

Master's degree preferred

Tools

SQL

Python

R

Job description

Minimum qualifications:

• 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 experience with a Master's degree.

Preferred qualifications:

• 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.

About the job:

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 enable product areas to efficiently consume ML resources for training and serving models.

In this high-visibility role, you will collaborate with Capital Engineering, Finance, PMs, and executive leadership to ensure the scalable and cost-effective deployment of ML compute across Google. Leveraging strong technical and analytical skills, you will uncover opportunities to improve efficiency through data transparency, software stack enhancements, user engagements, and service innovations like pricing and product tiers.

To succeed, you must be a strategic, agile problem solver who navigates ambiguity, acts with bias to action, and builds strong cross-functional relationships. You will partner closely with AI and Compute Enablement leads, regularly presenting findings to AI2 leadership.

Your work will directly, influence how Google optimizes investment, scaling ML infrastructure globally to meet the soaring demands of Google's ML products and research. You will engage with senior executives across Platforms, Research, Finance, and PA PARM teams to perfectly align our services with user needs.

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

US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

Responsibilities:

• Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Help solve problems, narrowing down multiple options into the best approach, and take ownership of open-ended ambiguous business problems to reach an optimal solution.
  
• Build new 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 the cross-functional/organizational leadership team. Translate analysis results to business insights or product improvement opportunities.
  
• Build and prototype analysis and business cases iteratively to provide insights at scale. Develop comprehensive knowledge of Google data structures and metrics, advocating for changes where needed for product development.
  
• Influence across teams to align resources and direction.