Unclassified - Atlanta, GA
posted 2 months ago
At Disney Entertainment & ESPN Technology, we are dedicated to reimagining the way audiences experience the world's most beloved stories. Our mission is to transform Disney's media business for the future by evolving our streaming and digital products, enhancing advertising and distribution capabilities, and delivering unmatched entertainment and sports content. As part of our team, you will play a crucial role in developing and maintaining recommendation and personalization algorithms for Disney Streaming's suite of applications, including Disney+ and Hulu. This position is an Individual Contributor role focused on content recommendations, where you will collaborate with Engineering, Product, and Data teams to apply machine learning methods to achieve strategic product personalization goals. In this role, you will lead the research, development, implementation, and optimization of recommendation and personalization algorithms. You will coordinate requirements and manage stakeholder expectations with Product, Engineering, and Editorial teams, ensuring that we meet key performance indicators (KPIs) for product areas. You will also be responsible for setting and meeting deadlines for both external and internal tools, such as offline evaluation tools for pre-production algorithms. As an Individual Contributor, you will help shape the roadmap for algorithmic work, addressing product requests for new recommendation features while driving larger company objectives in personalization and content recommendation. Your responsibilities will include utilizing cutting-edge machine learning methods to develop and implement algorithms for personalization and recommendation systems, maintaining deployed algorithms, and explaining methodologies to both technical and non-technical teams. You will also develop and maintain ETL pipelines using orchestration tools like Airflow and Jenkins, deploy scalable streaming and batch data pipelines to support petabyte-scale datasets, and establish best practices for algorithm development, testing, and deployment. Collaboration with product and business stakeholders will be essential to identify and define new personalization opportunities, improve data collection, experimentation, and analysis processes, and execute effective solutions for machine learning problems.