Research Data Scientist, Magic Eye

Minimum qualifications:

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

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

Preferred qualifications:

• 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 a PhD degree.

About the job:

Magic Eye's mission is to guide strategic decision making for Google's consumer products through data-informed insights, actionable user metrics, and definitions of success. The analysis team partners closely with cross-product area teams from Search, Ads, Android, YouTube, etc., and cross-functionally with teams from Engineering, Behavioral Economics, UX, Finance to bring methodology, data, and insights to Googlers and Google leads.

The analysis team is focused on cross-product user research and metric development to dig into Google's performance, including understanding Google's most important users, drivers of key trends, and layering in context from industry and the world. Key research questions include understanding the rapidly evolving GenAI landscape and user journeys across products and devices to identify cross-company strategic opportunities. This team also focuses on advanced decision making frameworks and tooling, ranging from observational, to causal inference, to experiments. We're all about scale to develop and leverage reliable, reproducible analyses for ourselves, our partners, and to drive aligned decision making across Google.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google. 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). Independently format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.