Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Staff Software Engineer, Data - Experimentation role.

Rezi rewrites your resume against DoorDash USA's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Staff Software Engineer, Data - Experimentation posting at DoorDash USA — free, in seconds.

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.

Responsibilities

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.

Requirements

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.

Skills

statistical methods
causal inference
machine learning
AI
experimentation
marketplace dynamics
product engineering
analytical SQL
variance reduction
ratio metrics
sequential testing
heterogeneous treatment effects
switchback designs
difference-in-differences
power analysis
multiple-testing correction
winsorization
quantile metrics
automated experiment readouts
risk detection
hypothesis generation

Location

United States
Illinois
Colorado
Remote

Work Type

Full-time

Experience Level

Staff

Salary/Compensations

$193,800—$285,000 USD

Benefits

401(k) plan with employer matching
16 weeks of paid parental leave
Wellness benefits
Commuter benefits match
Paid time off
Paid sick leave
Medical benefits
Dental benefits
Vision benefits
11 paid holidays
Disability and basic life insurance
Family-forming assistance
Mental health program
Flexible paid time off/vacation for salaried roles
80 hours of paid sick time per year for salaried roles

About the Company

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.

Equal Opportunity

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.
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.