What are the responsibilities and job description for the Research Data Scientist, YouTube Emerging Experiences position at YouTube?
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: San Bruno, CA, USA; Mountain View, CA, USA.
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.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. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
Minimum qualifications:
- Master'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 3 years of work experience with a PhD degree. - Experience with forecasting/time series.
Preferred qualifications:
- 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.
- Experience in the gaming or XR industry.
- Experience with feed-based applications and user funnels.
About the job
In this role, you will be a part of YouTube Data Science, a team that directly influences and informs YouTube’s product and engineering leadership as we have a long history of working on projects that are at the heart of the business and have a seat at the table when it comes to the decisions that drive YouTube's continued success. The Data Science team advises on strategy, metrics, and product changes that improve these 0 to 1 experiences for our users. Our mission is to improve decisions at YouTube with science.
Emerging Experiences and Community (EMCO) builds new experiences that are at the intersection of creation and consumption. Through our work on YouTube’s new and emerging consumer experiences, we empower both our viewers and creators to engage more deeply with one another, building and fostering communities.
The US base salary range for this full-time position is $174,000-$252,000 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.
Responsibilities
- Engage with stakeholders across cross-functional projects and team settings to identify and clarify business or product questions to answer, while providing feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
- Leverage custom data infrastructure or existing data models as appropriate, using specialized knowledge to design and evaluate models that mathematically express and solve defined problems with limited precedent.
- Work with the engineering and product teams to create new metrics, maintain classifiers, enable insights, and drive data-driven decision making.
- Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python), formatting, re-structuring, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
- Support launch decisions through experimental design and analysis.