Welcome to Peacock, the dynamic new streaming service from NBCUniversal. Here you'll find more than a job. You'll find a fast-paced, high-flying team for unique birds that want to be at the epicenter of technology, sports, news, tv, movies and more. Our flock works hard to connect people to what they love, each other and the world around them by creating shared experiences through culture-defining entertainment.
As a company, we embrace the power of difference. Our team is committed to creating an organization that champions diversity and inclusivity for all by curating content and a workforce that represents the world around us. We continue to challenge ourselves and the industry by being customer-centric, data-driven creatures of innovation. At Peacock, we are determined to forge the next frontier of streaming through creativity, teamwork, and talent.
Here you can fly to new heights!
As part of the Peacock Decision Sciences team, the Director of Data Science will be responsible for creating machine learning solutions for NBCU's video streaming service including but not limited to, the recommender systems, streaming content predictive modeling and MarTech.
In this role, the Director of Data Science will manage a team of data scientists in the design and execution of advanced analytical solutions using advanced data science methodologies including collaborative filtering, deep learning, reinforcement learning, on-line modeling etc. and lead collaboration with business owners and engineering team 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.
• Advanced (Master or PhD) degree with specialization in Statistics, Computer Science, Data Science, Economics, Mathematics, Operations Research or another quantitative field or equivalent.
• 7+ years of combined experience in advanced analytics in industry or research.
• 3+ years of experience managing a team.
• Experience with commercial recommender systems or a lead role in an advanced research recommender system project.
• Working experience with deep learning, particularly in the areas different form the computer vision. 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.
• Proficiency in Python. Java or Scala is a plus.
• Experience in data processing using SQL and PySpark
• Good understanding of algorithmic complexity of model training and testing, particularly for real-time and near real-time models.
• Advanced experience in media analytics and application of data science to the content streaming and TV industry.
• Good understanding of reinforcement learning algorithms.
• Knowledge of enterprise-level digital analytics platforms (e.g. Adobe Analytics, Google Analytics, etc.).
• Experience with television ratings and digital measurement tools (Nielsen, Rentrak, ComScore etc.).
• Experience with large-scale video assets.
• Ability to build trust across the organization and socialize solutions and identified data insights
• Pride and ownership in your work and confident representation of your team to other parts of NBCUniversal.
Jobcode: Reference SBJ-rv7w2m-3-233-219-62-42 in your application.