Senior Staff Data Scientist, AI Data Intelligence

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  
• 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.
  

About the job

As a Senior Staff Data Scientist, you will serve as a key innovation driver within our AI Data organization. You will leverage your expertise in the full life-cycle of data for large-scale AI models (specifically Large Language Models (LLMs): from collection and distillation to precision curation and refinement.

In this role, you will be instrumental in conceiving, architecting, and deploying novel data solutions that directly enhance AI performance. You will translate complex and ambiguous data challenges into strategic opportunities that deliver quantifiable business value. You will collaborate with executive engineering and product stakeholders.

The US base salary range for this full-time position is $248,000-$349,000 + 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 complex and ambiguous data science problems with particular focus on model evaluation and fine-tuning data for Large Language Models.
• 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 acquisition, and insights.

Solve complex and ambiguous data science problems with particular focus on model evaluation and fine-tuning data for Large Language Models.Solve complex and ambiguous data science problems with particular focus on model evaluation and fine-tuning data for Large Language Models.Design and deploy novel data acquisition and quality improvement techniques for foundational models.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.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).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 acquisition, and insights.Develop new methodologies to improve the performance of Google's models through better training data, including data acquisition, and insights.Develop new methodologies to improve the performance of Google's models through better training data, including data acquisition, and insights.