Research Data Scientist, YouTube Search and Viewer AI

Google•San Bruno, CA

6h•$147,000 - $210,000

About The Position

YouTube 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. At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.

Requirements

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.

Nice To Haves

Experience with machine learning and experiment design principles.
Experience with recommender systems.

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.