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.
- Experience in operations research, game theory, or machine learning.
- Experience in digital advertising or brand advertising.
Preferred qualifications:- PhD degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
About the jobAdvertising serves as the economic backbone of the YouTube platform that engages billions of users globally through an ever-growing and diversifying content library. YouTube advertisers range from global and local businesses, trying to broaden their outreach among the internet audience, to those seeking immediate commercial engagement from interested users. The first group are referred to as Brand Advertisers, and they allocate their marketing budgets to a wide spectrum of channels, spanning both traditional (e.g. TV, print, billboards etc.) and digital media. The Brand ads team is responsible for designing optimal solutions that would help these advertisers achieve their marketing objectives on YouTube. Given the relative nascency of programmatic digital platforms such as YouTube in the marketing portfolio of brand advertisers, our team of multi-skilled software engineers, data scientists, and product managers manage foundational problems that present a mix of technical complexity and business headroom.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (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, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
- Build optimization solutions encompassing targeting relevance, creative recommendation, bidding optimization, and impact measurement.
