Senior ML Resource Efficiency Data Scientist-Scale Compute

Google; Sunnyvale (CA); On-site; USD 163,000 - 236,000; Full time

Job summary

Google is seeking a senior data science professional to advance its ML compute efficiency across Google Cloud. You will analyze data, build scalable models, and partner with Capital Engineering, Finance, PMs, and executives to drive cost-effective deployment of ML resources globally.

You will shape analytics, pricing models, and service improvements while presenting findings to AI2 leadership and aligning with user needs across Platforms, Research, and PA PARM teams.

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.

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 KPIs to support business reviews with cross-functional leadership. Translate analysis results to business insights or product improvement opportunities.
• Build and prototype analysis and business cases iteratively to provide insights at scale. Develop knowledge of Google data structures and metrics, advocating for changes where needed for product development.
• Influence across teams to align resources and direction.

Skills

Analytics experience

Statistical analysis

Coding (Python, R, SQL)

Education

Bachelor's degree in quantitative field

Master's degree in quantitative field

Tools

SQL

Python

R

Job description

Google is seeking a senior data science professional to advance its ML compute efficiency across Google Cloud. You will analyze data, build scalable models, and partner with Capital Engineering, Finance, PMs, and executives to drive cost-effective deployment of ML resources globally.

You will shape analytics, pricing models, and service improvements while presenting findings to AI2 leadership and aligning with user needs across Platforms, Research, and PA PARM teams.