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# Machine Learning Scientist 5 - Ads Demand Science New

- **Company:** Netflix, Inc.
- **Location:** Reading, MA, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, SQL Databases, Agentic-AI, Information Technology
- **Published:** October 4, 2026
- **Apply:** https://www.gamesjobsdirect.com/job/netflix-game-studio/machine-learning-scientist-5-ads-demand-science/361375

## About the Role

* Experience with operations research.
 * Experience with AI engineering or agentic systems.

Who You Are:

 * Advanced degree (PhD or Master's) in Computer Science, Statistics, Mathematics, or a related quantitative field.
 * Experience building and shipping ML models or systems in production.
 * Curious and motivated to learn across ML domains, not attached to a single specialty.
 * Strong collaborator who's energized by building alongside others, not just independently.
 * Comfortable with ambiguity, able to take ownership with minimal oversight.
 * Proficient in Python and SQL.
 * Enthusiastic about and compatible with Netflix culture.

## Description

The Demand Science team within Ads Data Science & Engineering plays a crucial role in Netflix Ads growth by equipping the business to efficiently attract, activate, and scale advertiser demand. As a machine learning scientist in Media Planning, you'll work across the ML models and AI agents that turn advertiser briefs into optimized, ready-to-buy media plans. We're looking for a generalist who's curious, motivated to learn, and eager to build alongside the rest of the team, not someone who's already settled into one corner of ML.

What You'll Do:

 * Design and ship production ML models and conversational AI agents that parse advertiser requests and help sellers plan and compare media plan options.
 * Build and evolve the systems that evaluate and improve agent and model decision quality over time.
 * Partner with scientists and engineers across the media planning recommendation and agent workstreams to ship and iterate on new capabilities.
 * Dig into ambiguous problems, propose approaches, and drive them from idea to production.
 * Iterate on launched models and agents based on performance data and seller feedback.
 * Communicate clearly with both technical and non-technical stakeholders.