Product Data Scientist, YouTube Search and Viewer AI

About the role

MINIMUM QUALIFICATIONS:

• Bachelor's degree in Statistics, Data Science, Mathematics, Physics,

Economics, Operations Research, Engineering or a related quantitative field.

• 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.

PREFERRED QUALIFICATIONS:

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

ABOUT THE JOB:

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 of the LLM revolution,

and in a sweet spot between 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 changing 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 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.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $138000 - $197000 (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-project and team settings to identify

and clarify business or product questions to answer.

• 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.

• Provide feedback to translate and refine business questions into analysis,

evaluation metrics or mathematical models.