Senior Staff Data Scientist Manager, AI Data

Socket.dev; Mountain View (CA); On-site; USD 262,000 - 364,000; Full time

Job summary

Google in Mountain View, CA, is hiring a data scientist within the Machine Learning, Systems and Cloud AI group to improve data quality for ML models, including LLMs. You will work with model teams to define, measure, and promote data quality practices, and influence product direction with rigorous analyses.

Responsibilities include handling large datasets, developing metrics, presenting insights to executives, and driving data-driven decisions.

Qualifications

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

Responsibilities

• Work with large, data sets. Conduct analysis that includes data gathering and requirements specification, processing, cleaning and curation, analysis, visualization, ongoing deliverables, and presentations.
• Represent analysis to stakeholders and organization executives in order to share insights, influence product direction and answer difficult questions regarding data quality measurement and impact on model performance.
• Define key metrics that are statistically sound and meaningful to measure data quality for data in various shapes and forms, as well as to measure progress of customer engagement.
• Research and develop analysis and optimization methods to improve the quality of Google's ML portfolio and applications, including LLM model and training data planning.
• Conduct independent research and advance the state of understanding in how data impacts ultimate quality of large language models and creating spend optimization priorities with data acquisition.

Skills

Analytics experience

Coding (Python, R, SQL)

Statistical analysis

Data querying

People management

Education

Master's degree in Statistics or related quantitative field

Tools

Python

SQL

R

Databases

Job description

Minimum qualifications:

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

Preferred qualifications:

• 12 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 10 years of work experience with a PhD degree.
• 6 years of experience as a people manager within a technical leadership role.

About the job:

As a part of Machine Learning, Systems and Cloud AI (MSCA), we believe that high quality data is key to building better Machine Learning (ML) models, especially in the era of Large Language Models (LLMs). We work directly with model teams to define, measure, and improve data quality, promote best practices, and increase data availability and awareness. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Work with large, data sets. Conduct analysis that includes data gathering and requirements specification, processing, cleaning and curation, analysis, visualization, ongoing deliverables, and presentations.
• Represent analysis to stakeholders and organization executives in order to share insights, influence product direction and answer difficult questions regarding data quality measurement and impact on model performance.
• Define key metrics that are statistically sound and meaningful to measure data quality for data in various shapes and forms, as well as to measure progress of customer engagement.
• Research and develop analysis and optimization methods to improve the quality of Google's ML portfolio and applications, including LLM model and training data planning.
• Conduct independent research and advance the state of understanding in how data impacts ultimate quality of large language models and creating spend optimization priorities with data acquisition.