Lead Data Scientist at NBCUniversal

Brentford, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

22 Jan, 26

Salary

0.0

Posted On

24 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Science, Statistics, Deep Learning, Python, SQL, PySpark, Reinforcement Learning, Data Mining, Google Cloud Platform, TensorFlow, Graph Methodologies, Foundation Models, GenAI Technologies, Collaboration, Data Processing

Industry

Entertainment Providers

Description

Company Description 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 theme parks and consumer experiences. We own and operate leading entertainment and news brands, including NBC, NBC News, MSNBC, CNBC, NBC Sports, Telemundo, NBC Local Stations, Bravo, USA Network, and Peacock, our premium ad-supported streaming service. We produce and distribute premier filmed entertainment and programming through Universal Filmed Entertainment Group and Universal Studio Group, and have world-renowned theme parks and attractions through Universal Destinations & Experiences. NBCUniversal is a subsidiary of Comcast Corporation. 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. Comcast NBCUniversal has announced its intent to create a new publicly traded company ('Versant') comprised of most of NBCUniversal's cable television networks, including USA Network, CNBC, MSNBC, Oxygen, E!, SYFY and Golf Channel along with complementary digital assets Fandango, Rotten Tomatoes, GolfNow, GolfPass, and SportsEngine. The well-capitalized company will have significant scale as a pure-play set of assets anchored by leading news, sports and entertainment content. The spin-off is expected to be completed during 2025. Job Description Position Overview: As part of the Peacock Data Science team, the Lead Data Scientist will be responsible for creating recommendation and personalization solutions for one or more verticals of Peacock Video Streaming Service. You’ll collaborate with an international, cross-functional team of engineers, architects, product managers and analysts to identify and prioritize enhancements to Peacock’s personalization models. Your responsibilities will touch everything from feature creation to model evaluation and deployment, and you’ll have opportunities to work on cutting edge machine learning like foundation models and reinforcement learning. Responsibilities include, but are not limited to: Work with a group of data scientists in the development of recommendation and personalization 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 organization. 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. Qualifications Qualifications/Requirements: Advanced (Master or PhD) degree with specialization 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. Experience with multi-billion record datasets and leading projects that span the disciplines of data science and data engineering. Experience with large-scale video assets Team oriented and collaborative approach with a demonstrated aptitude and willingness to learn new methods and tools. Additional Information As part of our selection process, external candidates may be required to attend an in-person interview with an NBCUniversal employee at one of our locations prior to a hiring decision. NBCUniversal's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law. If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation. You can submit your request to [email protected].

Responsibilities

The Lead Data Scientist will create recommendation and personalization solutions for Peacock Video Streaming Service. Responsibilities include developing models, driving data collection, and collaborating with cross-functional teams.