Job Description
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 applying advanced statistical methods (e.g., causal inference, forecasting, predictive modeling, experimental design) to real-world business problems.
Preferred qualifications:
- Experience operating with high autonomy, identifying project roadmaps, and mentoring junior team members.
- Familiarity with creator economies, subscription business models, pricing strategy, or two-sided marketplaces.
- Demonstrated track record of cross-functional leadership and driving product direction through data science in a fast-paced environment.
- Ability to manage ambiguity and translate open-ended business problems into rigorous, mathematically formulated quantitative frameworks.
- Strong communication and storytelling skills, with the ability to explain complex statistical concepts to non-technical stakeholders (Product Managers, Engineering Leaders, Business Strategy teams).
About the job
In this role, you will join the Creator Data Science team, whose focus is to understand the growth and behavior of YouTube's various creator ecosystem, from large traditional media companies to individual creators who are just "getting started" and to build the tools, systems, and policies that help them grow. You will collaborate across the Creator organization and format teams (e.g., Shorts, Live) to build a holistic understanding of the creator ecosystem and monetization.Responsibilities
- Partner closely with engineering, product, and data science peers to reach ambitious Channel Membership growth targets, developing complex statistical models for pricing optimization, subscriber lifetime value, churn prediction to drive business growth.
- Collaborate with data science peers to go beyond standard metrics, leading quantitative research into user and creator behavior to uncover structural opportunities for the Channel Memberships ecosystem, supporting launch recommendations through advanced experimental design and causal inference analysis.
- Translate technical data problems into clear business recommendations, presenting findings to multiple levels of stakeholders through intuitive visual displays of quantitative information.
- Leverage custom data infrastructure or existing data models, using specialized knowledge to design and evaluate models to mathematically solve defined problems with limited precedent.
- Gather information, business goals, priorities, and organizational context around the questions to answer, and existing and upcoming data infrastructure.
