Research Data Scientist, Magic Eye

Google•Mountain View, CA

7d•$147,000 - $210,000

About The Position

Magic Eye guides strategic decision making for Google's consumer products through data-informed insights, actionable user metrics, and definitions of success. The analysis team partners with cross-product teams including Search, Ads, Android, and YouTube, and works cross-functionally with Engineering, Behavioral Economics, UX, and Finance to deliver methodology, data, and insights to Googlers and Google leads. The team conducts cross-product user research and develops metrics to understand Google's performance, important users, drivers of key trends, and relevant industry and world context. Research includes the evolving GenAI landscape and user journeys across products and devices to identify strategic opportunities across Google. The team also develops advanced decision-making frameworks and tools, including observational analysis, causal inference, and experiments, with an emphasis on reliable, reproducible analyses that support aligned decision making. Individual pay is determined by job-related skills, experience, and relevant education or training. The stated U.S. compensation range is $147,000–$210,000 USD, plus a 15% bonus target, equity, and benefits.

Requirements

Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
At least 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases, or statistical analysis; alternatively, a PhD degree.

Nice To Haves

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

Responsibilities

Collaborate with stakeholders in cross-project and team settings to identify and clarify business or product questions.
Use custom data infrastructure or existing data models as appropriate, applying specialized knowledge; design and evaluate models to mathematically express and solve defined problems with limited precedent.
Gather information about the questions to answer, business goals, priorities, organizational context, and existing and upcoming data infrastructure.
Own gathering, extracting, and compiling data across sources using relevant tools such as SQL, R, and Python; format, restructure, or validate data for quality and review datasets to ensure they are ready for analysis.
Provide feedback to translate and refine business questions into tractable analyses, evaluation metrics, or mathematical models.