below marketmost similar roles pay hereabove market
This role pays more than
60%
of similar roles. Most pay
$187,762–$254,187
— the blue band above.
At the midpoint, this role pays about
$239k
versus about
$221k
for comparable roles.
Based on 240 similar postings.
Employer
About DoorDash, Inc
DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States.
DoorDash, Inc currently has
97 open roles
on FindRole.
Listed pay typically runs
$163,800–$240,900
across 96 roles with salary data.
The Staff Applied Scientist, Experimentation and Causal Inference joins the Decision Systems team to provide scientific leadership for the Experimentation Platform. This role involves defining and advancing statistical, causal inference, machine learning, and AI capabilities to power high-quality decision-making across a three-sided marketplace. You will own the statistical contract for experiment analysis, develop and productionize advanced methods like CUPED/CUPAC, ratio-metric and delta-method inference, sequential testing, and switchback designs, and build robust measurement techniques for high-noise data. Key responsibilities include reviewing analytical SQL, mentoring technical contributors, and translating complex methods into scalable platform capabilities. You will utilize skills in causal inference, variance reduction, and production data pipelines to solve marketplace interference problems and improve product velocity through rigorous, trustworthy analysis.
What does a Applied Scientist earn?
Median $208800 from 61 postings across 10 companies.
Set the scientific direction for the experimentation platform and the Decision Systems roadmap.
Own the statistical contract for experiment analysis, including metrics, populations, and exposure windows.
Review analytical SQL and validate statistical parity to ensure production systems implement contracts correctly.
Develop and productionize advanced methods like CUPED, sequential testing, and difference-in-differences.
Build robust measurement techniques for high-scale, high-noise, three-sided marketplace data.
Advance AI-assisted analysis workflows for automated experiment readouts and risk detection.
Advise and mentor cross-functional teams on experimental design, causal analysis, and metric strategy.
What we're looking for
6+ years of industry experience or equivalent applied research experience in statistical, causal inference, machine learning, or experimentation systems.
Deep expertise in experimentation and causal inference methods like variance reduction, ratio metrics, sequential testing, and quasi-experimental designs.
Strong coding and data skills 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.
Proven track record of setting technical direction, influencing cross-functional teams, and mentoring senior technical contributors.
Ability to communicate complex technical concepts clearly to scientific, engineering, product, and executive audiences.
Ability to balance methodological rigor with product velocity, engineering constraints, and customer needs.