The role
From Google's own posting.
YouTube Data Science consists of a large analyst team that influences and informs YouTube’s product and engineering leadership teams. 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.
Within YouTube Data Science, the Music and Premium Data Science team is the home for all music products and podcasts at Google, along with efforts to grow the paid users (Premium, Nitrate, Commerce, and Growth). It is a critical area both for expanding Google's business through paid subscriptions growth, as well as showcasing innovation. This is the landing spot for GenAI audio and transforming the product using AI tools.
As a Data Scientist within the YouTube Premium team, you will work alongside premium and music Data Scientists, ProdOps, Finance, PM, Marketing, BD/partnerships, Engineering and UXR partners to uncover actionable data-driven insights that inform the product roadmaps and strategy, create models and define metrics for improved user experiences, and model growth dynamics in premium user base to support the M&P business.
You will need to have a background or a flair for topics related to economics, especially economics of paid subscription services.
The Music and Premium experience for YouTube users involves no ads, backgrounding, downloads (for offline experiences), and many others. This role involves understanding levers of YouTube premium subscriber growth, building robust and scalable tools to understand, manage the trajectory of the business, while balancing subscriber growth and profitability.
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 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
Drive operational diagnoses in the premium business, such as reasons for changes in paid preserving, trial to paid conversion and the implications for various areas in the subs business (lifetime calue, acquisition and experiments etc.).
Adjust prices which require understanding of the impact on existing subs (churn). Involve in improving methods to estimate price elasticity and play a key role in the mission control team which coordinates price increases.
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:
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.
Ability to read and apply quantitative methodologies from books, papers, etc.
Ability to take open-ended problems, provide better definition and eventually solutions.
Ability to communicate well (and the so what) with data.
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.