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YouTube BizOrg Music and Media Team is looking to shape the future of YouTube by working closely with its business leaders to steer relationships with partners and empower Music and Media partners and users to grow together on YouTube.

Requirements

• Experience in causal inference, A/B testing, statistical modeling, or machine learning.
• Experience in programming with SQL and Python, and in leveraging ML or statistical libraries (e.g., TensorFlow, Scikit-learn, XGBoost, Keras, Pandas).
• Experience in Natural Language Processing (NLP), Large Language Models (LLMs), Generative AI, and R.
• Publications related to Data Science, posters or presentations in AI/ML conferences, or patents.
• Knowledge of causal inference, Bayesian statistics, and machine learning.
• Experience with data engineering, business intelligence, and data science capabilities.
• Familiarity with experimental and observational techniques.

Responsibilities

• Design and execute A/B experiments and causal studies to address critical business questions.
• Leverage advanced statistical models to find business insights in experimental and observational data.
• Present and communicate actionable insights and recommendations to executives and cross-functional partners.
• Serve as a peer reviewer and consultant for causal studies across the organization.
• Determine the best approach for solving problems, and to communicate clearly with decision-makers who may or may not have an excellent technical background.
• Use a large set of tools from experimental to observational techniques, touching on a broad range of problems from different functional and product areas.
• Conduct causal studies and help stakeholders implement data driven decisions.

Other

• Master's degree in a Science, Technology, Engineering, or Mathematics field or equivalent practical experience.
• 3 years of experience in Data Science.
• PhD in a Science, Technology, Engineering, or Mathematics field.
• Creative problem-solving and stakeholder management skills.
• Ability to draw conclusions from data, break down technical concepts into simple terms, connect with, and present to, technical and non-technical stakeholders, and recommend actions.