Data Scientist, Product, AI Data

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) (or 2 years of work experience with a Master's degree).

Preferred qualifications:

• 3 years of experience in scripting, statistical analysis (e.g., R, Stata, SPSS, SAS), developing and managing metrics, and evaluating programs/products.
  
• 1 year of experience working in a complex, matrixed organization.
  

We believe that high quality data is key to building better AI models, especially in the era of Large Language Models (LLMs). We work directly 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 AI Data, 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.

The US base salary range for this full-time position is $122,000-$178,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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.

• Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct end-to-end analysis that includes data gathering and requirements specification, processing, cleaning and curation, analysis, visualization, ongoing deliverables, and presentations.
• Regularly share/present 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. 
• Define key metrics that are statistically sound and meaningful to measure data quality for data in various shapes and forms, as well as to measure progress of customer engagement.