Senior Data Scientist, Generative AI, Google Cloud

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
• 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 3 years of work experience with a PhD degree.
  

Preferred qualifications:

• Master's degree or PhD in Statistics, Computer Science, Engineering or Mathematics.
  
• 8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of experience with a PhD degree.

Experience articulating business questions and using mathematical techniques to arrive at an answer using available data. Experience translating analysis results into business recommendations.

Experience with large datasets in a distributed system.

Experience with human evaluation data such as surveys.

Familiarity with modern machine learning techniques, including sequence/transformer modeling experience in NLP/Vision/speech domains.

About the job

We believe that high-quality data is key to building AI models, especially in the era of Large Language Models (LLMs). We work with model and product teams to measure and improve data quality, collect and generate high quality data, develop evaluation methodologies, and enhance model performance.

As a part of Machine Learning (ML), Systems and Cloud AI (MSCA), we are positioned to build best data practices throughout Google, working with teams like Google DeepMind (GDM) and Cloud AI, and push the frontiers of GenAI.

Responsibilities

• Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct analysis that includes data gathering and requirements specification, processing, cleaning and curation, analysis, visualization, ongoing deliverables, and presentations.
  
• Share analysis to relevant stakeholders and organization executives in order to share insights, influence product direction and answer difficult questions regarding data quality measurement and impact on model performance.
  
• Build and prototype analysis pipelines iteratively to provide insights at scale. Work closely with product teams to incorporate important analysis into existing framework and tools.
  
• Interact cross-functionally with a wide variety of product and model teams. Work closely with engineers to identify opportunities for, design, and assess improvements of data quality and model performance.