Staff Research Data Scientist, AI Data Intelligence

Be a distinct contributor, with a particular focus on Human Data Collection for Large Language Models (LLMs) and address complex data science problems.Design and deploy novel data acquisition and quality improvement techniques for foundational models.Utilize AI models and tools as integral components for evaluating, synthesizing, and understanding complex datasets.Act as a critical technical partner, collaborating closely with Research, Engineering, and Product teams (Cloud AI Data and Google DeepMind).Develop new methodologies to improve the performance of Google's models through better training data, including data collection, and insights.Minimum qualifications:Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of experience with a PhD degree.Preferred qualifications:10 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of experience with a PhD degree.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 the most resourceful minds, define data excellence, and make a tangible impact on the future of intelligent systems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefitsLearn more about benefits at Google.Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of experience with a PhD degree.