Senior Scientist, Experimentation Platform

DoorDash, Inc.; San Francisco (CA); On-site; USD 194,000 - 285,000; Full time

Benefits offered by this job

Equity grants

401(k) with matching

Paid parental leave

Wellness benefits

Commuter benefits

Paid time off

Medical, dental, and vision

Job summary

DoorDash is seeking a Staff Applied Scientist within the Decision Systems team in San Francisco to define and advance statistical, causal inference, ML, and AI capabilities powering high‑quality experimentation and platform decisions. You will work on challenging problems at the intersection of experimentation, marketplace dynamics, ML, and product engineering.

You will set scientific direction, own the contract for experiment analysis, and ensure production systems faithfully implement

Qualifications

• 6+ years of industry experience or equivalent applied research in 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, including production data pipelines and complex analytical SQL review for statistical correctness.
• Experience translating statistical or ML methods into reliable, scalable platform capabilities in collaboration with engineering 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: balancing 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.
• Review analytical SQL, validate statistical parity, and ensure correctness across 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 marketplace data with power analysis, multiple testing correction, winsorization, and robust measurement techniques.
• Advance AI‑assisted analysis workflows, including automated readouts and risk detection, with rigorous statistical methods.
• Mentor data scientists, product teams, operators, and engineers on experimental design and interpretation.

Skills

Statistical analysis

Causal inference

Machine learning

Experiment design

Python

SQL

Production systems

Tools

Python

SQL

Spark

Job description

DoorDash is seeking a Staff Applied Scientist within the Decision Systems team in San Francisco to define and advance statistical, causal inference, ML, and AI capabilities powering high‑quality experimentation and platform decisions. You will work on challenging problems at the intersection of experimentation, marketplace dynamics, ML, and product engineering.

You will set scientific direction, own the contract for experiment analysis, and ensure production systems faithfully implement