Research Data Scientist, YouTube Gaming Discovery

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 job YouTube Data Science influences and informs YouTube’s product and engineering leadership. The Gaming Discovery team helps gamers find YouTube content whether it's videos about gaming (e.g., gaming-related content) or Playables (e.g., mini-games playable within the YouTube app).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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $211000 (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.