Data Scientist, Research, AI Data

Minimum Qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.

Preferred Qualifications:

• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.

About The Job

Imagine being at the core of the AI revolution, where your expertise directly fuels the most advanced large language models. We are the driving impact behind high-quality data – the essential ingredient for unlocking unprecedented AI breakthroughs. Partner with the greatest 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.

The US base salary range for this full-time position is $141,000-$202,000 + 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.
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

• Solve challenging data science problems related to AI models evaluation and training.
• Contribute to the design and implementation of 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.
• Contribute to new methodologies to improve the performance of Google's models through better training data, including data acquisition and insights.