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.
  

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 working on a consumer-facing product.
  
• Experience with effective experimental design, training and in areas such as data analysis, survey design, statistical modeling.
  
• Familiarity with statistical tools/software packages (e.g., Python, SQL etc.).
  
• Ability to apply an appropriate quantitative/statistical model to real-world business problems.

About the job

We're building AI Mode, integrating state-of-the-art Generative AI directly into the Search experience. Working alongside engineers and PMs, you'll work on complex challenges, push the boundaries of AI application, and architect novel ways for billions to interact with Google daily. This is your chance to redefine the interaction between users and knowledge on a global scale.

In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.

Responsibilities

• Interact cross-functionally with a wide variety of teams, and work closely with product management and engineering leads to identify growth and quality opportunities for the product.
• Conduct analyses of datasets, applying statistical methods to solve problems, and presenting actionable recommendations to stakeholders.
  
• Drive innovation by adapting to frequent pivots and comfortably managing the ambiguity of this rapidly evolving field.
  
• Own projects end-to-end, covering problem definition, metrics development, data extraction and manipulation, visualization, creation, implementation of analytical/statistical models, and presentation to stakeholders.
  
• Translate granular metric analyses into executive-level strategic narratives, combining high-level insights with the analytical depth to address multi-layered leadership inquiries.