Lead Data Scientist

NBCUniversal•New York, NY

1d•$145,000 - $175,000•Hybrid

About The Position

As part of Peacock’s Data Science team, the Senior Lead Data Scientist creates recommendation and personalization solutions for one or more verticals of Peacock’s video streaming service. The role collaborates with an international, cross-functional team of engineers, architects, product managers, and analysts to identify and prioritize improvements to Peacock’s personalization models. Work spans feature creation, model evaluation, and deployment, with opportunities to apply machine learning approaches such as foundation models and reinforcement learning.

Requirements

Advanced (Master’s or PhD) degree specializing in Statistics, Computer Science, Data Science, Machine Learning, Mathematics, Operations Research, or another quantitative field, or equivalent.
At least 5 years of combined machine learning experience 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.
Proficiency in Python.
Experience processing data using SQL and PySpark.
Experience working with foundation models and other generative AI technologies.

Nice To Haves

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

Responsibilities

Work with data scientists to develop recommendation and personalization models using statistical, machine learning, and data mining methodologies.
Drive the collection and manipulation of new data and refinement of existing data sources.
Translate complex problems and solutions for all levels of the organization.
Collaborate with software and data architects to build real-time and automated batch implementations of data science solutions and integrate them into the streaming service architecture.
Drive innovation in the statistical and machine learning methodologies and tools used by the team.