Software Engineer, Data - Experimentation

About the Team The Decision Systems team helps Door Dash teams make faster, more rigorous, and more trustworthy decisions. We build the analytical systems, statistical methods, and product experiences that power experimentation, causal inference, rollout analysis, metric interpretation, and business impact measurement across Door Dash’s three-sided marketplace. Curie is Door Dash’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.

Our work spans experimentation platforms, causal inference, variance reduction, switchback experimentation, metric systems, and AI-assisted analysis workflows. We partner closely with data scientists, engineers, product managers, and operators across Door Dash to translate advanced methods into reliable, scalable systems that improve decision quality and product velocity. Some of the work done in the team was published in articles such as:
Design-Aware Variance Reduction for Switchback Experiments: A Comparative Study, Power-Optimal Covariate Adjustment for Switchback Experiments, CUPED on Steroids:
Multivariate Covariate Adjustment for Switchback Experiments.

We are looking for a Staff Applied Scientist to provide scientific leadership for Door Dash’s Experimentation Platform. In this role, you will define and advance the statistical, causal inference, machine learning, and AI capabilities that power high-quality decision-making across Door Dash. You will work on challenging problems at the intersection of experimentation, marketplace dynamics, machine learning, and product engineering. Your work will shape how Door Dash teams design experiments, analyze results, interpret noisy marketplace outcomes, and make faster, more trustworthy decisions.

This is a high-leverage role for a scientist who enjoys both deep technical work and broad cross-functional influence—partnering with engineers, data scientists, product managers, and operators to translate cutting‑edge methods into reliable platform capabilities used across the company.

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, identifying where better science, tooling, or automation can materially improve decision quality and velocity. Own the statistical contract for experiment analysis, including how metrics, populations, exposure windows, covariates, ratio metrics, and edge cases are defined. 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 product ionize 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, including power analysis, multiple‑testing correction, winsorization, quantile metrics, and other robust measurement techniques. Advance AI‑assisted analysis workflows, including trustworthy automated experiment readouts, risk detection, and hypothesis generation, with the same rigor expected of statistical methods.

Advise and mentor data scientists, product teams, operators, and engineers on experimental design, metric strategy, causal analysis, and interpretation of results.

We’re excited about you because…