Staff Applied Scientist, Experimentation Platform Lead

DoorDash, Inc.; California; On-site; USD 180,000 - 240,000; Full time

Job summary

DoorDash, Inc. is seeking a Staff Applied Scientist to provide scientific leadership for Curie, its Experimentation Platform within the Decision Systems team.

You will define and advance statistical, causal inference, ML, and AI capabilities powering high-quality decisions across DoorDash's three-sided marketplace. Your work will shape how teams design experiments, analyze results, interpret outcomes, and make faster, more trustworthy decisions.

Qualifications

• 6+ years of industry experience in statistical, causal inference, machine learning, or experimentation systems with measurable business impact.
• Deep expertise in experimentation and causal inference, with production experience in 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.

Responsibilities

• 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 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, 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.

Skills

Statistical & ML expertise

Causal inference

Experimentation systems

Production data pipelines

Tools

SQL

Python

Job description

DoorDash, Inc. is seeking a Staff Applied Scientist to provide scientific leadership for Curie, its Experimentation Platform within the Decision Systems team.

You will define and advance statistical, causal inference, ML, and AI capabilities powering high-quality decisions across DoorDash's three-sided marketplace. Your work will shape how teams design experiments, analyze results, interpret outcomes, and make faster, more trustworthy decisions.