Staff Applied Scientist, Experimentation and Causal Inference

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

About The Team

The Decision Systems team helps DoorDash teams make faster, more rigorous, and more trustworthy decisions. We build the analytical systems, statistical methods, and product experiences that power experimentation, causal inference, rollout analysis, metric interpretation, and business impact measurement across DoorDash’s three-sided marketplace. Curie is DoorDash’s experiment analysis product within Decision Systems. It sits at the center of how teams move from hypothesis to analysis to confident action, helping decision makers understand experiments, rollouts, metric movements, and marketplace impact while preserving scientific rigor, velocity, trust, and interpretability. Our work spans experimentation platforms, causal inference, variance reduction, switchback experimentation, metric systems, and AI-assisted analysis workflows. We partner closely with data scientists, engineers, product managers, and operators across DoorDash to translate advanced methods into reliable, scalable systems that improve decision quality and product velocity. Some of the work done in the team was published in articles such as: Design-Aware Variance Reduction for Switchback Experiments: A Comparative Study, Power-Optimal Covariate Adjustment for Switchback Experiments, CUPED on Steroids: Multivariate Covariate Adjustment for Switchback Experiments.

About The Role

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.

We’re Excited About You Because You Have…

• 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.

Compensation

The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job‑related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market‑dependent and may be modified in the future. In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.

DoorDash cares about you and your overall well‑being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family‑forming assistance, and a mental health program, among others.

See Below For Paid Time Off Details

• For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.
• For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).

About DoorDash

At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door‑to‑door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods.

DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well‑being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.

Our Commitment to Diversity and Inclusion

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

Statement of Non‑Discrimination

In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on “protected categories,” we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce – people who identify as women, non‑binary or gender non‑conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently‑abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non‑discrimination.

We’re committed to a flexible, accessible, and diverse hiring process

Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.

If you need any accommodations, please inform your recruiting contact upon initial connection.

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

We expect this position to be filled by 10-17-2026

Salary Range (United States, including Illinois and Colorado)

$193,800—$285,000 USD