Data Scientist, Research, PhD, Early Career, 2026 Start

Google is an engineering company that hires individuals with a wide range of technical skills to tackle significant technological challenges. As a Data Scientist, you will evaluate and enhance Google's products by collaborating with a multi-disciplinary team on various problems, employing scientific and statistical methods to improve product development and user experience.

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, restructure, or validate data to ensure quality and review the dataset to ensure it is ready for analysis

Skills

• PhD in a quantitative discipline (e.g., Statistics, Biostatistics, Computer Science, Applied Mathematics, Operations Research, Economics) or another discipline involving experimental design and quantitative analysis of data
• 1 year of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree
• Experience in one or more of the following areas: coding (e.g., C/C++, Python, Java), databases and querying (e.g., SQL, MySQL, MapReduce, Hadoop), statistical analysis (e.g., R, Stata, SPSS, SAS), or hypothesis testing
• Experience with causal inference methods (e.g., split-testing, instrumental variables, difference-in-difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators)
• Experience with statistical programming languages, controlled experimentation, causal inference, and statistical data analysis (e.g., linear models, multivariate analysis, stochastic models, sampling methods, etc.)
• Applied experience with machine learning on large datasets
• Ability to start full-time role in 2026

Benefits

• Bonus
• Equity
• Benefits

Company Overview

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