Director, Data Science, Algorithmic Targeting

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 unequalled 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 Decision Sciences team, this Director, Data Science will be responsible for creating algorithmic targeting solutions across the app, including promotion pricing.In this role, the Director, Data Science will build and deploy machine learning solutions at scale to empower and optimize leadership’s and our research partners’ abilities to lift our enterprise metrics by best leveraging 1st party data to personalize the user experience and our apps’ dynamic surfaces.Our Principals and Directors serve as a team leader in advanced statistical and machine learning methodologies and lead both data scientists and projects to create analytical solutions for multiple business verticals.Responsibilities include, but are not limited to:Own and deliver results on existing and new complex projects related to the development of targeting models using machine learning and causal uplift modelingServe as a team expert in statistical methods and applied machine learningDrive the collection and manipulation of new data and the refinement of existing data sourcesPossess a strong understanding of the differences between inferential, predictive, and prescriptive models and experience applying methods in all three areasWork with business stakeholders to define priorities, approaches and business requirements for data science solutions and experimentsTranslate complex problems and solutions to all levels of the organizationManage processes, platforms, and people in the algorithmic targeting spaceDrive innovation of the methodologies and tools used by the team