Staff Data Scientist, YouTube Shopping and Horizontal

Google•San Bruno, CA

5d•$197,000 - $291,000

About The Position

At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we 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 $197,000-$291,000 + bonus + equity + 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 .

Requirements

Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.

Nice To Haves

10 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree.
Experience with classifiers.

Responsibilities

Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions, providing feedback to translate and refine them into analysis, evaluation metrics, or mathematical models.
Leverage custom data infrastructure or existing data models using specialized knowledge to design and evaluate models that mathematically express and solve defined problems.
Gather information, business goals, priorities, and organizational context around the questions to answer, including the existing and upcoming data infrastructure.
Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python), formatting, re-structuring, or validating data to ensure quality, and readiness for analysis.