Senior Product Data Scientist, AI Data

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 8 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) (or 5 years of work experience with a Master's degree).

Preferred qualifications:

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

About the job

In this role, you will join the Data Intelligence team in the AI Data organization to provide the human expert data that trains, assesses, and elevates the Gemini family of models. You will partner with team members to define standards for data excellence and develop intelligent systems that are safe, helpful, and capable.

The US base salary range for this full-time position is $163,000-$237,000 + 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

• Analyze post-training data for Large Language Models (LLMs), conduct loss pattern analysis, and evaluate data quality.
• Manage data usage in the model evolution life-cycle, from model training and tuning to evaluation and the gathering of user interaction signals.
• Manage data science problems related to AI models evaluation and training. Contribute to improve the efficiency, impact and quality of the human data collection pipeline.
• Utilize AI models and tools as integral components for evaluating the quality and impact of human data on AI model performance.
• Work cross-functionally with Research, Engineering, and Product teams (Cloud AI Data and DeepMind).