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.
About the jobImagine being at the core of the AI revolution, where your expertise directly fuels the most advanced Large Language Models. We are the driving team behind high-quality data - the essential ingredient for unlocking unprecedented AI breakthroughs. Partner with resourceful minds, define data excellence, and make a tangible impact on the future of intelligent systems. If you're passionate about data and want to shape the trajectory of AI, your journey starts here.
As a Senior Staff Research Data Scientist, you will be a key driver of innovation. This role demands deep expertise in machine learning and a strong foundation in core data science methodologies. You'll be instrumental in conceiving and deploying solutions, directly contributing to the advancements of AI. Your ability to translate challenges into strategic opportunities will be critical as you collaborate closely with senior stakeholders.
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, complex data sets and solve complex and ambitious data science problems.
- Share/present to executive stakeholders and executives, effectively defend your work, and influence strategy and product direction.
- Interact cross-functionally with a wide range of product and model teams. Work closely with Product Management and engineering to identify opportunities.
- Define key metrics to measure success in various shapes and forms.
- Develop new methodologies to improve the performance of Google's models through better training data, including data acquisition, and insights.
