Machine Learning Scientist 5- Forecasting Aggregation

Overview

Build interpretable machine learning models that forecast advertising campaign delivery outcomes and augment Netflix's simulation-based forecasting engine. Own modeling and prototyping end to end while partnering with ML engineers to productionize and monitor models.

What you'll do

• Build and iterate on supervised models predicting delivery risk, reach, frequency, and contention.
• Incorporate supply-side signals into demand-side campaign outcome models.
• Design offline and online evaluations for accuracy, robustness, and improvement over simulation.
• Create reusable features from ad-serving logs, campaign attributes, and supply signals.
• Develop explainable outputs for sales and media-planning stakeholders.
• Partner with ML engineers to deploy models and monitor production health and drift.
• Communicate technical decisions and results to technical and non-technical audiences.

What you'll need

• 5+ years of relevant experience building machine learning models on large-scale data.
• PhD or Master's degree in a quantitative field such as Statistics, Mathematics, or Computer Science.
• Expertise in supervised learning, including gradient-boosted trees and regression.
• Strong Python and SQL programming skills.
• Experience with feature engineering and standard ML lifecycle practices.
• Understanding of ad-serving, campaign delivery, inventory, and campaign performance metrics.
• Ability to work independently and communicate technical and statistical concepts clearly.

Nice to have

• Ads experience, especially at a DSP, SSP, or publisher-side ad platform.
• Familiarity with Metaflow or comparable large-scale ML tooling.
• Experience shipping and monitoring production ML systems with ML engineers.
• Experience creating data products, dashboards, or explainability tooling.
• Experience applying GenAI to developer or research productivity.

Details

• Remote in the United States.
• Annual salary range: $466,000–$750,000.
• Full-time role.