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 Deep Mind).

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).