Data Scientist 5 – Infrastructure Experimentation

Job Description: • 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 Requirements: • Experienced in experimentation methodology and causal inference, with a strong foundation in A/B testing, treatment effect estimation, and statistical significance • Experienced in building and maintaining machine learning models in production, including the full lifecycle of training, evaluation, monitoring, and continuous improvement • Fluent in Python and SQL, with experience engineering data pipelines and working with large-scale data systems • A strong collaborator who thrives in horizontal roles with broad stakeholder surfaces, comfortable influencing decisions through data and analysis rather than direct authority • An exceptional communicator who can flex between technical and non-technical audiences, translating statistical concepts for software engineers and business leaders alike • Comfortable with messy, incomplete data environments and able to balance short term execution with a drive to improve data quality over time • A strong product thinker who views data science outputs as products, taking an end-to-end ownership mindset from data quality through to user adoption • Comfortable with ambiguity, and thrive with minimal oversight and process • Curious about infrastructure systems; prior experience in the infrastructure domain is a strong plus but not required; the ability and motivation to learn is essential Benefits: • Health Plans • Mental Health support • 401(k) Retirement Plan with employer match • Stock Option Program • Disability Programs • Health Savings and Flexible Spending Accounts • Family-forming benefits • Life and Serious Injury Benefits • Paid leave of absence programs • Full-time hourly employees accrue 35 days annually for paid time off