Staff Software Engineer, Data - Experimentation
DoorDash USA · California, CA; San Francisco, CA; Seattle, WA; New York,NY
About this role
**Staff Software Engineer, Data — Experimentation** **About the Team** The Decision Systems team helps DoorDash teams make faster, more rigorous, and more trustworthy decisions. They build analytical systems, statistical methods, and product experiences that power experimentation, causal inference, rollout analysis, metric interpretation, and business impact measurement across DoorDash’s three-sided marketplace. Curie is DoorDash’s experiment analysis product within Decision Systems. It sits at the center of how teams move from hypothesis to analysis to confident action—helping decision makers understand experiments, rollouts, metric movements, and marketplace impact while preserving scientific rigor, velocity, trust, and interpretability. **About the Role** We’re looking for a **Staff Applied Scientist** to provide scientific leadership for DoorDash’s **Experimentation Platform**. In this role, you’ll define and advance the statistical, causal inference, machine learning, and AI capabilities that power high-quality decision-making across DoorDash. Your work will shape how DoorDash teams design experiments, analyze results, interpret noisy marketplace outcomes, and make faster, more trustworthy decisions. This is a high-leverage role with broad cross-functional influence—partnering with engineers, data scientists, product managers, and operators to translate cutting-edge methods into reliable platform capabilities. **You’re excited about this opportunity because you will…** - Set the scientific direction for Curie and shape the Decision Systems roadmap with engineering and product partners. - Own the statistical contract for experiment analysis (metrics, populations, exposure windows, covariates, ratio metrics, and edge cases). - Ensure production systems faithfully implement those contracts by reviewing analytical SQL, validating statistical parity, and raising the bar for correctness across engine migrations. - Develop and productionize advanced methods such as CUPED/CUPAC, ratio-metric and delta-method inference, sequential testing, heterogeneous treatment effects, switchback designs, and difference-in-differences for marketplace interference. - Build approaches that hold up in high-scale, high-noise, three-sided marketplace data (power analysis, multiple-testing correction, winsorization, quantile metrics, and other robust measurement techniques). - Advance AI-assisted analysis workflows (trustworthy automated experiment readouts, risk detection, and hypothesis generation) with the same rigor as statistical methods. - Advise and mentor data scientists, product teams, operators, and engineers on experimental design, metric strategy, causal analysis, and interpretation. **We’re excited about you because you have…** - **6+ years** of industry experience (or equivalent applied research) developing statistical, causal inference, machine learning, or experimentation systems with measurable business impact. - Deep expertise in experimentation and causal inference, including production experience with methods such as variance reduction, ratio metrics, sequential testing, heterogeneous treatment effects, marketplace interference, or quasi-experimental designs. - Strong coding and data skills, including the ability to reason about production data pipelines and review complex analytical SQL for statistical correctness. - Experience translating statistical/ML methods into reliable, scalable platform capabilities in close partnership with
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