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# Staff Software Engineer, Data - Experimentation

- **Company:** DOORDASH, INC.
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Machine Learning, Operational Databases, SQL Databases, Power Analysis (Cryptography)
- **Published:** October 18, 2026
- **Apply:** https://www.thejobnetwork.com/job/47ab7c79-56b7-4c52-b46a-3e08672598c3/staff-software-engineer-data-experimentation

## About the Role

- 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 close 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.
- The ability to communicate complex technical concepts clearly to scientific, engineering, product, and executive audiences.
- A bias for impact: you know how to balance methodological rigor, product velocity, engineering constraints, and customer needs.


## Description

We are looking for a Staff Applied Scientist to provide scientific leadership for DoorDash'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 DoorDash. You will work on challenging problems at the intersection of experimentation, marketplace dynamics, machine learning, and product engineering.

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 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 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.