Data Scientist
US$160,000 – US$180,000 / yr base salary
Posted 18 Aug 2026
Advertised as “Senior Data Scientist”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Remote
| Base salary | US$160,000 – US$180,000 / yr |
|---|
Field of studyComputer Science, Engineering, Mathematics, Physics, Science, Statistics
Study levelMaster's
WHO WE ARE
Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.
THE ROLE
The machine learning team sits at the heart of our technology and delivers on the core brand promise of bringing rocket science to make advertising more rational and effective.
Zeta Global’s technology platform supports a real-time bidding system that handles over 100 billion events every day, a reporting system that aggregates and analyzes terabytes of data in real-time, and a learning system that applies machine learning and artificial intelligence techniques to over 40 petabytes of data. These systems all work in harmony so that Zeta Global can serve the right advertisement to the right user at the right time.
The Artificial Intelligence and Machine Learning team’s work sits at the core of our technology as the key decision-making agent for our systems. We research and develop the algorithms and models that make our software intelligent, as well as design, implement, and maintain the code that powers our production systems.
Some of the problems we work on include:
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Propensity Modelling: How do you use the rich data we have about users to make predictions? How do you handle conversion events that are extremely rare, such as purchasing a car? How do you deal with cold start problems?
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Bidding Strategies: How do you translate a click or conversion score into a bid? What if it’s a first price auction, second price auction, or auctions with dynamic floors? How do you take into account pacing for hour of day, day of week, and special holidays like Thanksgiving and Christmas?