Data Scientist, Research, YouTube

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
• 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 statistical analysis, data science techniques and frameworks, SQL, Python or R.

Preferred qualifications:

• Experience in reducing the spread of bad content/engagement/actors on YouTube and improving the safety of our users and creators/partners.
• Experience with human rater evaluation and with GenAI, ML or LLM evaluation.
• Experience with sampling techniques (e.g., importance sampling) and their application.
• Ability to communicate well with data and translating data science concepts to a wide variety of stakeholders.
• Ability to balance applied quantitative methods and practical impact.

About the job

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.

Responsibilities

• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
  
• Solve complex sampling problems to help us efficiently estimate rare events.
• Develop scalable, investigative and interpretable tooling for faster iteration on LLM-as-Raters models for content moderation.
• Balance safety and sustainability, help/drive decisions to improve efficient use of machine labels for detection and their quality.
• Understand and measure improvements of safety efforts for Youth on YouTube.