Staff Data Scientist, AI Data Intelligence bei Google
Google · Sunnyvale, Vereinigte Staaten Von Amerika · Onsite
- Senior
- Optionales Büro in Sunnyvale
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
- 2 years of experience working across data or AI/ML domains.
Preferred qualifications:
- 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.
- Experience with human eval data.
About the job
As a Staff Data Scientist, you will become a key innovation driver within our AI Data organization. You will use your expertise in the full lifecycle of data for large-scale AI models (specifically 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. Your ability to translate complex, ambiguous data challenges into strategic opportunities and quantifiable business value will be critical as you collaborate with executive engineering and product stakeholders.
Responsibilities
- Solve complex and ambiguous data science problems with particular focus on model evaluation and fine-tuning data for Large Language Models (LLMs).
- 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.
