Lead Data Scientist

NBCUniversal•New York, NY

16h•Hybrid

About The Position

As part of the Peacock Data Science team, the Senior 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.

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.
Strong working experience with deep learning.
Proficient in Python.
Experience in data processing using SQL and PySpark.
Experience working with foundation models and other GenAI technologies.

Nice To Haves

Experience in media analytics and application of data science to the content streaming and TV industry.
Good understanding of reinforcement learning algorithms and graph methodologies.
Experience with multi-billion record datasets and leading projects that span the disciplines of data science and data engineering.
Experience with Kubeflow, Airflow, and Databricks (or equivalent).
Experience with large-scale video assets.
Team oriented and collaborative approach with a demonstrated aptitude and willingness to learn new methods and tools.

Responsibilities

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.