Senior Staff Research Data Scientist, AI Data Research

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 will 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
.

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.

Responsibilities
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.