Lead Data Scientist

Description

The Peacock Data Science team builds recommendation and personalization solutions for one or more verticals of the Peacock video streaming service.

You will work with an international group of engineers, architects, product managers and analysts to find and rank improvements to Peacock's personalization models. The work covers feature creation, model evaluation and deployment, and includes early-stage machine learning such as foundation models and reinforcement learning.

The company is one of the largest media and entertainment businesses in the world. It is a subsidiary of Comcast Corporation and runs the Peacock streaming service among its film, television, news and theme park operations.

Responsibilities

• Work alongside a group of data scientists to build recommendation and personalization models using statistical, machine learning and data mining methods.
• Lead the gathering and preparation of new data, and improve existing data sources.
• Explain complex problems and their solutions to people at every level of the organization.
• Work with software and data architects to build real-time and scheduled batch versions of data science solutions and connect them to the streaming service's architecture.
• Push forward the statistical and machine learning methods and tools the team uses.

Requirements

• A master's or PhD in statistics, computer science, data science, machine learning, mathematics, operations research or another quantitative field, or equivalent experience.
• At least 5 years of combined machine learning experience in industry or research.
• Experience with commercial recommender systems, or with leading an advanced research recommender project.
• Substantial hands-on work with deep learning.
• Proficiency in Python.
• Experience processing data with SQL and PySpark.
• Experience with foundation models and other generative AI technologies.
• Nice to have: experience in media analytics or applying data science to content streaming or TV.
• Nice to have: a solid grasp of reinforcement learning algorithms and graph methods.
• Nice to have: experience with datasets of billions of records, and leading projects that span data science and data engineering.
• Nice to have: experience with Kubeflow, Airflow and Databricks, or equivalents.
• Nice to have: experience with large-scale video assets.
• Nice to have: a collaborative, team-first approach and a clear willingness to learn new methods and tools.

Conditions

This is a hybrid role requiring in-office work at least four days per week. The company may change in-office requirements at any time.

The salary range is $145,000 to $175,000, with eligibility for bonus and long-term incentive pay.

Equal opportunity: the company values an inclusive workforce.

Benefits

• Eligible for company-sponsored benefits including medical, dental and vision insurance, a 401(k), paid leave, tuition reimbursement and a range of discounts and perks.