Staff Software Engineer, Data - Experimentation
Core
Define and advance statistical, causal inference, machine learning, and AI capabilities for DoorDash's Experimentation Platform to enable high-quality decision-making across the three-sided marketplace.
Role type
Staff Applied Scientist (Experimentation & Causal Inference)
Builds
Curie, DoorDash's experiment analysis product powering hypothesis-to-action workflows
Domain
Marketplace dynamics, experimentation platforms, causal inference, AI-assisted analysis
Deliverable
production ML models
Required skills
Causal inference, experimentation design, statistical methods, machine learning, SQL, production data pipelines, cross-functional leadership, technical strategy
Preferred skills
Switchback experimentation, CUPED/CUPAC, heterogeneous treatment effects, AI-assisted analysis workflows
Technologies
SQL, Python (implied by ML/Engineering context), experimental frameworks
Responsibilities
Set scientific direction for the experimentation roadmap; own the statistical contract for experiment analysis; ensure production systems implement statistical contracts correctly; develop and productionize advanced statistical methods; build robust measurement techniques for high-scale data; advance AI-assisted analysis workflows; advise and mentor teams on experimental design and interpretation.
Seniority
Staff, hands-on IC with broad influence
