Staff Data Scientist, Experimentation Platform

Doordashusa; San Francisco, Seattle, New York (CA, WA, NY); On-site; USD 194,000 - 285,000; Full time

Benefits offered by this job

401(k) plan

Paid parental leave

Medical, dental, vision

Job summary

DoorDash is seeking a Staff Applied Scientist for its Decision Systems team (Curie) to drive experimentation, causal inference, ML, and AI capabilities across the three‑sided marketplace. You will define statistical contracts, review production analytics, and push advanced methods into scalable platform features used company-wide.

You will collaborate with engineers, data scientists, and product partners to balance rigor with velocity, guiding cross-functional teams and mentoring senior

Qualifications

• 6+ years of industry experience developing statistical, causal inference, ML or experimentation systems with measurable business impact.
• Deep expertise in experimentation and causal inference with production experience in variance reduction, ratio metrics, sequential testing, heterogeneous treatment effects, marketplace interference, or quasi-experimental designs.
• Strong coding and data skills with production data pipelines and ability to review complex analytical SQL.
• Experience translating statistical or ML methods into reliable, scalable platform capabilities in collaboration with engineering and product teams.
• Track record of setting technical direction and influencing cross-functional teams.
• Ability to communicate complex technical concepts to scientific, engineering, product and executive audiences.
• Bias for impact balancing rigor, 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 and definitions of metrics, populations, exposure windows, covariates, and edge cases.
• Review analytical SQL and ensure statistical parity across engine migrations.
• Develop and productionize advanced methods such as CUPED/CUPAC, ratio‑metric inference, sequential testing, and DIF for marketplace interference.
• Build robust measurement approaches for high-scale, high-noise three‑sided marketplace data.
• Advance AI‑assisted analysis workflows with rigorous readouts and risk detection.
• Advise and mentor data scientists, product teams, and engineers on experimental design and interpretation.

Skills

Experimentation

Causal inference

Machine learning

SQL proficiency

Production systems

Mentoring

Tools

SQL

Job description

DoorDash is seeking a Staff Applied Scientist for its Decision Systems team (Curie) to drive experimentation, causal inference, ML, and AI capabilities across the three‑sided marketplace. You will define statistical contracts, review production analytics, and push advanced methods into scalable platform features used company-wide.

You will collaborate with engineers, data scientists, and product partners to balance rigor with velocity, guiding cross-functional teams and mentoring senior