Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  
• 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.
  

Preferred qualifications:

• 12 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 10 years of work experience with a PhD degree.
  

About the job

We are looking for a Data Scientist (DS) who can work in a rapidly evolving tech landscape with novel methodologies, to generate actionable insights for product teams and leaders.

In this role, you will engage deeply with Google DeepMind and the core of Google’s AI capabilities. You will shape the investigative directions and strategies for Gemini, revealing actionable insights into how it integrates with Google’s complex software engineering ecosystem. Additionally, you will drive an understanding of how various agentic capabilities affect software and model development workflows, leveraging these insights to optimize complex systems at a Google scale.

Responsibilities

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
  
• Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
  
• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
  
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.