About this role
Imagine 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 an impact on the future of intelligent systems.
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.
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 (https://www.google.com/about/careers/applications/benefits/).
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
- 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.
- Experience in Deep Learning, Infrastructure Systems, or Reinforcement Learning.
- Work with 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.