Staff Software Engineer, Data - Experimentation

DoorDash•San Francisco, CA

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 is the team’s experiment analysis product, supporting experiment and rollout analysis, metric interpretation, and assessment of marketplace impact while maintaining scientific rigor, velocity, trust, and interpretability. The team works across experimentation platforms, causal inference, variance reduction, switchback experimentation, metric systems, and AI-assisted analysis, partnering with data scientists, engineers, product managers, and operators across DoorDash. The posting describes a role providing scientific leadership 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 involving experimentation, marketplace dynamics, machine learning, and product engineering, and translates advanced methods into reliable platform capabilities used across the company. The position sets scientific direction for Curie and shapes the Decision Systems roadmap with engineering and product partners. It owns the statistical contract for experiment analysis and helps ensure production systems implement that contract correctly. It also develops and productionizes advanced experimental methods, advances trustworthy AI-assisted analysis workflows, and advises and mentors technical and business partners on experimental design and interpretation.

Requirements

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 scientific direction for Curie and shape the Decision Systems roadmap with engineering and product partners.
Identify opportunities where improved science, tooling, or automation can improve decision quality and velocity.
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 across engine migrations.
Develop and productionize methods including CUPED/CUPAC, ratio-metric and delta-method inference, sequential testing, heterogeneous treatment effects, switchback designs, and difference-in-differences for marketplace interference.
Develop robust measurement approaches for high-scale, high-noise 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.