Lead Data Scientist - Recommender Systems

NBCUniversal is one of the world's leading media and entertainment companies. We create world-class content, which we distribute across our portfolio of film, television, and streaming, and bring to life through our global theme park destinations, consumer products, and experiences. We own and operate leading entertainment and news brands, including NBC, NBC News, NBC Sports, Telemundo, NBC Local Stations, Bravo, and Peacock, our premium ad-supported streaming service. We produce and distribute premier filmed entertainment and programming through our powerhouse film and television studios, including Universal Pictures, DreamWorks Animation, and Focus Features, and the four global television studios under the Universal Studio Group banner, and operate industry-leading theme parks and experiences around the world through Universal Destinations & Experiences, including Universal Orlando Resort, home to Universal Epic Universe, and Universal Studios Hollywood. NBCUniversal is a subsidiary of Comcast Corporation. Visit www.nbcuniversal.com for more information.

Our impact is rooted in improving the communities where our employees, customers, and audiences live and work. We have a rich tradition of giving back and ensuring our employees have the opportunity to serve their communities. We champion an inclusive culture and strive to attract and develop a talented workforce to create and deliver a wide range of content reflecting our world.

As part of the Peacock Data Science team, the Lead Data Scientist will be responsible for creating recommendation and personalisation solutions for one or more verticals of Peacock Video Streaming Service.

Collaborate with a global, cross-functional team of engineers, architects, product managers, and analysts to shape and advance Peacock's personalisation models. You'll drive the full lifecycle, from feature creation to model evaluation and deployment, while gaining the opportunity to innovate with cutting-edge machine learning foundation models and reinforcement learning techniques.

Responsibilities include, but are not limited to:

• Work with a group of data scientists in the development of recommendation and personalisation models using statistical, machine learning and data mining methodologies.

• Drive the collection and manipulation of new data and the refinement of existing data sources.

• Translate complex problems and solutions to all levels of the organisation.

• Collaborate with software and data architects in building real-time and automated batch implementations of data science solutions and integrating them into the streaming service architecture.

• Drive innovation of the statistical and machine learning methodologies and tools used by the team.

• Degree in Statistics, Computer Science, Data Science, Machine Learning, Mathematics, Operations Research or another quantitative field or equivalent.

• 5+ years of combined experience in machine learning in industry or research.

• Experience with commercial recommender systems or a lead role in an advanced research recommender system project.

• Working experience with deep learning and graph methodologies in machine learning. Strong experience with deep learning using TensorFlow.

• Experience implementing scalable, distributed, and highly available systems using Google Could Platform.

• Experience with Google AI Platform/Vertex AI, Kubeflow and Airflow.

• Proficient in Python. Java or Scala is a plus.

• Experience in data processing using SQL and PySpark.

• Experience working with foundation models and other GenAI technologies.

Desired Characteristics:

• Experience in media analytics and application of data science to the content streaming and TV industry.

• Good understanding of reinforcement learning algorithms.

• Team oriented and collaborative approach with a demonstrated aptitude and willingness to learn new methods and tools.