Research Data Scientist, Search Ads

About the role

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:

Ads Metrics is the Data Science team for Search ads. We support the SAGE (Search

Ads and Google Experience) organization in developing the most important ad

products at Google - from classic text ads, to rich shopping ads, to exciting

new products like Discovery ads. These products - the heart of Google’s business

driving annual business - and are complex, advanced, and they are rapidly

growing and evolving.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine

learning and Generative AI models like Gemini to power a multi-billion dollar

global business.

Our work directly impacts billions of users by protecting users from harm,

improving ad quality, and optimizing campaigns for advertiser

return-on-investment. We foster a culture of deep collaboration, partnering

closely with teams like Google Research and DeepMind to solve complex

challenges. Join us to work on state-of-the-art AI, take on problems at an

unparalleled scale, and build the next generation of advertising technology.

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

[https://www.google.com/about/careers/applications/benefits/].

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.