Machine Learning Scientist 5- Forecasting Aggregation

About the role

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

In 2022 we launched a new lower-priced, ad-supported tier, and we are building an in-house, world-class ad-tech ecosystem to give our members more choice and to offer advertisers a premium, better-than-linear-TV experience. We are looking for the founding members of this new business area for Netflix.

The Ads Forecasting team builds the predictive foundation of the Netflix ads business — the models that tell us, before a campaign ever runs, how much inventory is available and how a campaign will deliver. We forecast supply and demand across audiences, ad products, and formats, and we predict campaign outcomes such as maximum availability, delivery confidence, reach, and frequency, accounting for the ad-serving optimizations that shape delivery. Our forecasts power media planning, underwriting, budget planning, yield, and the public API.

This is a brand-new, foundational role. You will build the machine learning models that augment our simulation-based engine for predicting campaign delivery — turning a slow, rules-based simulation into fast, accurate, learnable models of how campaigns deliver against real inventory. You'll own the modeling and prototyping end-to-end and partner closely with our ML engineering team to take models to production.

In this role, you will:

- Build, prototype, and iterate on supervised machine learning models that predict campaign delivery outcomes — delivery risk, reach, frequency, and contention — to replace the current simulation engine.

- Model demand-side campaign outcomes while incorporating supply-side signals, so the models reason about how well available inventory matches what advertisers are trying to achieve (targeting, frequency caps, contention, pacing).

- Design rigorous offline and online evaluation frameworks to measure model accuracy, robustness to seasonality and distribution shift, and lift over the simulation baseline.

- Own feature engineering and contribute to the team's feature store — turning ad-serving logs, campaign attributes, and supply signals into reusable, well-documented features.

- Prioritize explainability and interpretability: your models' outputs must be defensible to sales and media-planning stakeholders making real booking and underwriting decisions.

- Partner with ML engineers to deploy models at scale and to monitor production model health and drift, feeding monitoring insights back into the next modeling iteration.

- Collaborate with cross-functional partners across product, engineering, and sales to define objectives, constraints, and trade-offs, and to drive adoption of ML-driven forecasts.