Staff Software Engineer, Data - Experimentation

DoorDash USA•Seattle, WA

3h•$193,800 - $285,000

About The Position

The Decision Systems team builds analytical systems, statistical methods, and product experiences that help DoorDash teams make faster, more rigorous, and more trustworthy decisions. Curie, DoorDash’s experiment analysis product within Decision Systems, supports the progression from hypothesis to analysis to confident action, helping teams understand experiments, rollouts, metric movements, and marketplace impact while maintaining scientific rigor, velocity, trust, and interpretability. The team’s work includes experimentation platforms, causal inference, variance reduction, switchback experimentation, metric systems, and AI-assisted analysis workflows, in close partnership with data scientists, engineers, product managers, and operators. The posting describes a scientific leadership role for DoorDash’s Experimentation Platform. The role defines and advances statistical, causal inference, machine learning, and AI capabilities that support decision-making across DoorDash. It addresses problems at the intersection of experimentation, marketplace dynamics, machine learning, and product engineering, shaping how teams design experiments, analyze results, interpret marketplace outcomes, and make decisions. The role partners across functions to translate advanced methods into reliable, scalable platform capabilities. The role is expected to be filled by October 17, 2026. Starting pay is based on job-related factors and work location; the national base pay range is $193,800–$285,000 USD. Equity grants are also offered. Benefits for regular employees include a 401(k) with employer matching, paid parental leave, wellness benefits, commuter benefits match, paid time off and sick leave, medical, dental and vision benefits, paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program.

Requirements

At least 6 years of industry experience, or equivalent applied research experience, developing statistical, causal inference, machine learning, or experimentation systems with measurable business impact.
Deep expertise in experimentation and causal inference, with production experience in 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 or machine learning methods into reliable, scalable platform capabilities in partnership with engineering and product teams.
A track record of setting technical direction, influencing cross-functional teams, and mentoring senior technical contributors without relying solely on formal authority.
Ability to communicate complex technical concepts clearly to scientific, engineering, product, and executive audiences.
Ability to balance methodological rigor, product velocity, engineering constraints, and customer needs.

Responsibilities

Set the scientific direction for Curie and shape the Decision Systems roadmap with engineering and product partners, identifying opportunities to improve decision quality and velocity through science, tooling, or automation.
Own the statistical contract for experiment analysis, including definitions for metrics, populations, exposure windows, covariates, ratio metrics, and edge cases.
Ensure production systems faithfully implement statistical contracts by reviewing analytical SQL, validating statistical parity, and improving correctness during engine migrations.
Develop and productionize advanced methods, including CUPED/CUPAC, ratio-metric and delta-method inference, sequential testing, heterogeneous treatment effects, switchback designs, and difference-in-differences for marketplace interference.
Build robust measurement approaches for high-scale, high-noise, three-sided marketplace data, including power analysis, multiple-testing correction, winsorization, and quantile metrics.
Advance AI-assisted analysis workflows, including trustworthy automated experiment readouts, risk detection, and hypothesis generation.
Advise and mentor data scientists, product teams, operators, and engineers on experimental design, metric strategy, causal analysis, and interpretation of results.