Staff Applied Scientist, Experimentation Platform

DoorDash, Inc.; California; On-site; USD 180,000 - 260,000; Full time

Job summary

DoorDash, Inc. seeks a Staff Applied Scientist to lead scientific direction for Curie, the Experimentation Platform within Decision Systems.

You will define and advance statistical, causal inference, ML, and AI capabilities powering high‑quality decision making across DoorDash. You will partner with engineers, data scientists, product managers, and operators to translate cutting‑edge methods into reliable platform capabilities used company-wide, shaping experiments, analyses, and actionability

Qualifications

• 6+ years of industry experience or equivalent applied research experience.
• Deep expertise in experimentation and causal inference with production experience in methods like variance reduction, ratio metrics, sequential testing, heterogeneous treatment effects, marketplace interference, or quasi‑experimental designs.
• Strong coding and data skills, including production data pipelines and reviewing complex analytical SQL.
• Experience translating statistical or ML methods into reliable, scalable platform capabilities with engineers and product teams.
• Track record of setting technical direction, influencing cross‑functional teams, and mentoring senior contributors.
• Ability to communicate complex technical concepts clearly to scientific, engineering, product, and executive audiences.
• Bias for impact: 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.
• Own the statistical contract for experiment analysis, including metrics, populations, exposure windows, covariates, ratio metrics, and edge cases.
• Ensure production systems faithfully implement contracts by reviewing SQL, validating statistical parity, and guiding engine migrations.
• Develop and productionize advanced methods such as CUPED/CUPAC, ratio‑metric and delta‑method inference, sequential testing, and DIF for marketplace interference.
• Build approaches for high-scale, high-noise three‑sided marketplace data, including power analysis, multiple-testing correction, and robust measurement techniques.
• Advance AI‑assisted analysis workflows with rigorous automated readouts, risk detection, and hypothesis generation.
• Advise and mentor data scientists, product teams, and engineers on experimental design, metric strategy, and interpretation.

Skills

Statistical methods

Causal inference

Machine learning

Experimentation systems

SQL proficiency

Tools

SQL

Job description

DoorDash, Inc. seeks a Staff Applied Scientist to lead scientific direction for Curie, the Experimentation Platform within Decision Systems.

You will define and advance statistical, causal inference, ML, and AI capabilities powering high‑quality decision making across DoorDash. You will partner with engineers, data scientists, product managers, and operators to translate cutting‑edge methods into reliable platform capabilities used company-wide, shaping experiments, analyses, and actionability