About the job

Google is and always will be an engineering company. We hire people with a broad set of technical skills who are ready to take on some of technology's greatest challenges and make an impact on millions, if not billions, of users. At Google, data scientists not only revolutionize search, they routinely work on massive scalability, storage solutions, large-scale applications, and entirely new platforms for developers around the world. From Google Ads to Chrome, Android to YouTube, social to local, Google engineers are changing the world one technological achievement after another.

As a Data Scientist, you will evaluate and improve Google's products. You will collaborate with a multi-disciplinary team of engineers and analysts on a wide range of problems. This position will bring scientific and statistical methods to the challenges of product creation, development, and improvement with an appreciation for the behaviors of the end user.

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.

The US base salary range for this full-time position is $141,000-$202,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or 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). Format, restructure, or validate data to ensure quality and review the dataset to ensure it is ready for analysis.

Minimum qualifications

• 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.

Preferred qualifications

• 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.