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# java python machine learning scala advertising

- **Company:** Netflix, Inc.
- **Location:** Pasadena, CA, United States (Remote available)
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Data Analysis, Python (Programming Language), Machine Learning, Operational Databases, Yield Optimization, Information Technology, Optimization Algorithms, Data Analytics, Marketplace
- **Published:** September 2, 2026
- **Apply:** https://www.workingnomads.com/job/go/1829405/

## About the Role

* Advanced degree (PhD or Master's) in Computer Science, Statistics, Mathematics, or related quantitative field.
 * Proficiency in Python, Scala or Java.
 * Deep knowledge of machine learning, optimization, and data analysis techniques.
 * Experience with prototyping and deploying algorithms using large-scale production data.
 * Strong business acumen and ability to translate technical results into business impact.
 * Experience in ad optimization stack, e.g. targeting, ranking, bidding..
 * Excellent communication and collaboration skills.

## Description

The Ad Marketplace team in Ads Data Science and Engineering plays a crucial role in Netflix's ad business growth. The mission for this team is to build a healthy, competitive, and innovative ad marketplace that balances long term Netflix revenue, member experience, and advertiser outcomes. The team will be responsible for ad auction design, dynamic pricing, member ad experience, and inventory / yield optimization. Our goal is to create innovative, data-driven solutions that deliver highly relevant ad experiences for our members and achieve impactful results for advertisers, all while upholding the exceptional quality and personalization characteristic of the Netflix experience., * Design and implement machine learning and optimization algorithms to improve ad quality and performance.

 * Build, train, and evaluate models on large-scale production data.
 * Develop online and offline evaluation frameworks to rigorously measure the impact of model and algorithm improvements.
 * Partner closely with the product team to define optimization objectives, constraints, and trade-offs that align with product and business goals.
 * Communicate technical decisions, trade-offs, and experiment results to both technical and non-technical stakeholders, driving understanding and adoption of ML-driven solutions.


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