Senior Lead Data Scientist — Personalization & Recommendations

NBCUniversal; New York (NY); Hybrid; USD 145,000 - 175,000; Full time

Benefits offered by this job

Hybrid: 4 days in office

Medical Insurance

Dental Insurance

Vision Insurance

401(k)

Paid Leave

Tuition Reimbursement

Employee Discounts

Job summary

NBCUniversal's Peacock Data Science team is seeking a Senior Lead Data Scientist to develop recommendation and personalization solutions for Peacock Video Streaming.

You will collaborate with an international team of engineers, architects, product managers and analysts to advance features, model evaluation, and deployment, including foundation models and reinforcement learning. Hybrid work available with strong compensation.

Qualifications

• Advanced degree (Master or PhD) in a quantitative field such as Statistics, CS, Data Science or Mathematics.
• 5+ years of machine learning experience in industry or research.
• Experience with commercial recommender systems or leading a recommender project.
• Strong experience with deep learning techniques.
• Proficient in Python programming.
• Experience with SQL and PySpark for data processing.
• Familiarity with foundation models and GenAI technologies.

Responsibilities

• Develop recommendation and personalization models.
• Collaborate with data scientists and engineers to improve data sources.
• Translate complex problems for all levels of the organization.
• Collaborate on real-time and batch deployments within streaming service architecture.
• Drive innovation in ML methodologies and tools.

Skills

Advanced degree

Machine learning

Recommender systems

Deep learning

Python

SQL PySpark

GenAI

Education

Master/PhD in quantitative field

Tools

Kubeflow

Airflow

Databricks

Job description

NBCUniversal's Peacock Data Science team is seeking a Senior Lead Data Scientist to develop recommendation and personalization solutions for Peacock Video Streaming.

You will collaborate with an international team of engineers, architects, product managers and analysts to advance features, model evaluation, and deployment, including foundation models and reinforcement learning. Hybrid work available with strong compensation.