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:- Experience with machine learning and experiment design principles.
- Experience with recommender systems.
About the jobYouTube Search is the second biggest search engine in the world. And its GenAI viewer experiences such as Ask YouTube is on track to become one of the largest AI products in the world. This role joins a growing team on the cutting edge of the LLM revolution, and in a sweet spot between a classic RecSys and future LLM-powered systems.
In this role, projects will range from analyzing experiments, to improving foundational search systems, to developing foundational signals. We are looking for someone flexible and comfortable working in a dynamic environment.
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 .
Responsibilities- Collaborate with stakeholders in cross-project and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into analysis, evaluation metrics or mathematical models.
- Use custom data infrastructure or existing data models. 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 and Python). Format, re-structure or validate data to ensure quality and review the dataset to ensure it is ready for analysis.
