• 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 • Extend our measurement framework to new metrics (e.g., latency) and new experiment types (e.g., infrastructure canary tests) • Champion an infrastructure lens within the broader experimentation community, helping shift culture toward reasoning about the full ROI and infrastructure impact of experiments • Connect with the larger analytics and experimentation communities at Netflix to bring visibility to our work and learn from others