Data Scientist, Advertising Products & Solutions

Job Summary

NBC Universal is seeking a Data Scientist for Advertising Products Solutions in Grand Central, Manhattan, US. This role will drive data‑powered insights and machine‑learning models to enhance ad products across NBCUniversal’s portfolio.

Key Responsibilities

• Design, develop, and deploy predictive models that optimize ad targeting, pricing, and inventory allocation.
• Collaborate with product managers, engineers, and analysts to translate business problems into analytical solutions.
• Analyze large‑scale advertising datasets, perform A/B testing, and generate actionable recommendations.
• Build dashboards and visualisations to communicate findings to stakeholders at all levels.
• Stay current with emerging data‑science techniques and apply them to improve ad‑tech performance.

Qualifications

• Advanced degree (M.S. or Ph.D.) in Computer Science, Statistics, Mathematics, or related field.
• 5+ years of experience in data science, preferably within advertising, media, or tech.
• Proficiency in Python or R, SQL, and big‑data platforms (Spark, Hadoop, Snowflake).
• Hands‑on experience with machine‑learning libraries (scikit‑learn, TensorFlow, PyTorch).
• Strong analytical mindset, communication skills, and ability to work in a fast‑paced, cross‑functional environment.

Compensation

Annual salary: $296,781.90

Location

Grand Central, Manhattan, United States

Frequently Asked Questions

What type of data will I work with as a Data Scientist at NBC Universal?

You will analyze large advertising datasets, including impression logs, user demographics, bidding data, and performance metrics to improve ad targeting and revenue.

Do I need a Ph.D. for this position?

A Ph.D. is preferred but not required; an M.S. in a quantitative field plus 5+ years of relevant experience is acceptable.

Is remote work an option for this role?

The position is based in Grand Central, Manhattan, and requires on‑site collaboration, though limited remote flexibility may be discussed.