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Senior Product Data Scientist, YouTube Shorts Creation
GoogleSingaporeSenior8+ years
Apply Minimum qualifications:
- Bachelor'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, performing statistical analysis, and coding (e.g., Python, R, SQL) or 5 years of work experience plus a Master's degree.
Preferred qualifications:
- 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 it has 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. Your mission is to improve decisions at YouTube with science.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $163000 - $237000 (USD) + 15% bonus target + equity + 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.
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