Senior Staff Data Scientist Manager, AI Data
Google · Mountain View, California, United States · $262K–$364K/yr
Software Development · 10,001+ employees
About the role
The role involves defining and measuring data quality for machine learning models, specifically focusing on large language models. The manager will lead analysis efforts, influence product direction, and conduct research to optimize data acquisition and model performance.
What they look for
Requirements
Candidates must have a master's degree in a quantitative field and at least 10 years of relevant work experience. Additionally, the role requires 6 years of experience in a people management or technical leadership capacity.
Benefits
Full 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.
