Senior Lead Data Scientist - Personalization & Recs (Hybrid)

African American Talent; New York (NY); Hybrid; USD 145.000 - 175.000; Vollzeit

Benefits dieser Stelle

Medical insurance

Dental insurance

Vision insurance

401(k)

Paid leave

Tuition reimbursement

Zusammenfassung

NBCUniversal, a world‑leading media company, is seeking a Senior Lead Data Scientist to build recommendation and personalization solutions for Peacock Video Streaming.

You will collaborate with an international, cross‑functional team to identify enhancements, drive feature creation, model evaluation and deployment, and explore foundation models and reinforcement learning.

Hybrid schedule with competitive benefits and opportunities for growth within a data-driven environment.

Qualifikationen

• Advanced degree in a quantitative field (Masters/PhD).
• 5+ years of ML experience in industry or research.
• Experience with commercial recommender systems or advanced research projects.
• Strong experience with deep learning.
• Proficient in Python; SQL and PySpark experience.
• Experience with foundation models and GenAI technologies.

Aufgaben

• Develop recommendation and personalization models for Peacock's streaming service.
• Collaborate with engineers, architects, product managers and analysts.
• Advance feature creation, model evaluation and deployment.
• Drive data collection and data source refinement.
• Innovate ML methodologies and tools used by the team.

Kenntnisse

Python

SQL

PySpark

Deep learning

Recommender systems

GenAI

Reinforcement learning

Data visualization

Ausbildung

Master's or PhD in Statistics or Data Science

Quantitative field

Tools

Kubeflow

Airflow

Databricks

Jobbeschreibung

NBCUniversal, a world‑leading media company, is seeking a Senior Lead Data Scientist to build recommendation and personalization solutions for Peacock Video Streaming.

You will collaborate with an international, cross‑functional team to identify enhancements, drive feature creation, model evaluation and deployment, and explore foundation models and reinforcement learning.

Hybrid schedule with competitive benefits and opportunities for growth within a data-driven environment.