Data Scientist 5 - Infrastructure Experimentation

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.

Everything that we build to delight our members, and all of the work we do to figure out how to do that better, relies on our extensive infrastructure, which spans what we rent from AWS to what we build ourselves (we operate our own custom-built content delivery network, Open Connect). Because this infrastructure is so central to everything we do, we want to ensure we are using it in the most effective and efficient way. Doing that well starts with great visibility into how we are using our infrastructure, and actionable metrics and analytics to help us take action as needed.

As a Senior Data Scientist focused on Infrastructure Experimentation, you will join a data org of Analytics Engineers, Data Engineers, and Data Scientists who partner with our infrastructure engineering teams to measure, model, and surface the infrastructure impact of product experiments. You will work closely with partners in data science, platform engineering, and experimentation platform teams to build and maintain models and experimentation capabilities that predict the cost and performance implications of A/B tests, enabling experiment owners to make more holistic decisions that balance member impact with infrastructure performance.

The ideal candidate will excel in experimentation design and evaluation, causal inference, and applied machine learning, with a passion for applying these skills within the infrastructure domain.

In this role, you will:

●Build and maintain machine learning models that predict the infrastructure cost impact of A/B experiments, translating experimentally observed signals (e.g., request volume changes) into business and system metrics (e.g. projected annualized costs)●Drive adoption of infrastructure metrics within the experimentation community through analysis, consultation with experiment owners, documentation, and training●Partner with platform teams (Observability, Experimentation Platform) to improve the quality and coverage of infrastructure usage data feeding our models