Research Data Scientist, GenAI Quality, YouTube

Research Data Scientist, GenAI Quality, You Tube

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In this role, you will help shape the future of the Creator Economy by ensuring You Tube rewards high‑value innovation over low‑quality, automated content. You will partner with stakeholders in the You Tube Partner Program to measure and mitigate the risks associated with synthetic media. You will be a core member of the working group studying the rise of generative media.

You will act as the bridge between detection and economics, using science to ensure our business generation encourages high‑quality GenAI content while defunding abusive, low‑effort generation.

At You Tube, 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.

The US base salary range for this full‑time position is $141,000–$202,000 plus bonus, equity, and benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

• Develop the methodology for measuring low‑quality synthetic content within the business generation ecosystem. Establish definitions and frameworks required to distinguish creative tool utilization from abusive mass‑production.
• Design methodologies to validate Large Language Model (LLM) performance on nuanced content tasks. Leverage LLMs to develop and deploy “hybrid” and “LLM‑first” metrics that distinguish between creative and repetitive content.
• Apply sampling theory and optimization techniques to our north star metrics for business generation safety.
• Own the process of gathering, extracting, compiling, and analyzing data across sources via relevant tools (e.g., SQL, R, Python).

• 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 Machine Learning.

• PhD 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.
• Experience in reading and applying quantitative methodologies from the academic literature.
• Expertise working with Large Language Models, GenAI Evaluation, AI tools, or Multimodal Machine Learning.

Not Applicable

Full‑time

Information Technology and Engineering

Information Services and Technology, Information and Internet

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.