Our Direct-to-Consumer (DTC) portfolio is a powerhouse collection of consumer-first brands, supported by media industry leaders, Comcast, NBCUniversal, and Sky. When you join our team, you’ll work across our dynamic portfolio including Peacock, NOW, Fandango, SkyShowtime, Showmax, and TV Everywhere, powering streaming across more than 70 countries globally. And the evolution doesn’t stop there. With unequaled scale, our teams make the most out of every opportunity to collaborate and learn from one another. We’re always looking for ways to innovate faster, accelerate our growth, and consistently offer the very best in consumer experience. But most of all, we’re backed by a culture of respect. We embrace authenticity and inspire people to thrive.
As part of the Direct-to-Consumer Recommendation Sciences team, the Director, Personalization & AI will be responsible for creating generative and classical machine learning solutions for NBCU’s video streaming service including but not limited to, content recommendations, product experience personalization, and personalized ad services.
In this role specifically, the you will lead a pod of data scientists through algorithmic solution design, rapid prototyping, and technical review for the ML/AI models underlying personalized user experiences and content promotion. You will be a key point of contact for cross-functional architects and software developers as they integrate personalized data products and capabilities across our suite of global and domestic products. You will work with a dynamic cohort of high calibar individuals across Product, Technology, Marketing, Content Discovery, Editorial, and more to build a state-of-the-art real-time video streaming service
Responsibilities include, but are not limited to:
- Build and manage a high-performance team of data scientists.
- Lead the team in the development of a recommendation system modeling and experimentation framework.
- Work with business stakeholders to define priorities, approaches and business requirements for the analytical solutions.
- Manage multiple priorities across business verticals and machine learning lifecycle projects.
- Lead work with engineering teams to define data science driven requirement and solutions for major initiatives and opportunities of the streaming service functionality.
- Drive innovation of the statistical and machine learning methodologies and tools used by the team. Lead improvements in machine learning lifecycle infrastructure.
- Drive a data science culture that inspires and motivates the team to succeed.