Minimum qualifications: - Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
Preferred qualifications: - 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.
- 3 years of experience as a people manager within a technical leadership role.
- Familiarity with modern machine learning techniques (including sequence/transformer modeling experience in NLP/Vision/speech domains).
About the jobAs a part of ML, Systems & Cloud AI (MSCA), we believe that high quality data is key to building better 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.
The US base salary range for this full-time position is $197,000-$291,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
Responsibilities - Work with large, complex data sets and solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct end-to-end analysis that includes data gathering and requirements specification, processing, cleaning and curation, analysis, visualization, ongoing deliverables, and presentations.
- Share/present analysis to relevant 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.
- Interact cross-functionally with a wide variety of product and model teams.
- 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.
