Staff Machine Learning Scientist, Applied Causal Inference

DoorDash; San Francisco (CA); On-site; USD 170,000 - 250,000; Full time

Benefits offered by this job

Equity grants

401(k) with employer matching

Paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Sick leave

Job summary

DoorDash seeks a Causal Machine Learning Engineer to build the causal ML backbone behind growth across New Verticals. You will design and productionize uplift and heterogeneous treatment effect models, connect experimentation with observational data, and develop evaluation frameworks for ranking, recommendations, and promotions.

You will work with ML engineers, economists, data scientists, product managers, and business leaders to translate causal insights into scalable decisions that impact

Qualifications

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Experience shipping production ML models or decision systems in large consumer marketplaces.
• Strong judgment on tradeoffs between randomized experiments and observational estimations.
• Comfort debating uplift methods and off-policy evaluation in production environments.
• Able to build reliable pipelines and partner with platform teams for production deployment.
• Capable of connecting methods to business decisions beyond offline metrics.

Responsibilities

• Design, build, and productionize causal ML systems that influence marketplace decisions across New Verticals.
• Develop uplift/heterogeneous treatment effect models for lifecycle value, promotions, and retention.
• Create counterfactual evaluation frameworks for ranking, recommendations, search, and interventions.
• Bridge experimentation, observational data, and ML decisioning to improve tradeoffs.
• Design surrogate metrics and early indicators to accelerate development without compromising health.
• Collaborate with econometrics and analytics leaders to choose robust methods.
• Translate causal models into production systems shaping ranking, budget, and growth.
• Raise the bar for causal reasoning and debugging in real marketplaces.

Skills

Causal inference

Production ML systems

Econometrics

Experimentation platforms

Uplift modeling

Contextual bandits

Off-policy evaluation

ML engineering

Product judgment

Cross-functional collaboration

Job description

About The Team

DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories. These businesses operate in high-dimensional, messy marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question.

About The Team

DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories. These businesses operate in high-dimensional, messy marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question.

About The Role

We are hiring a Causal Machine Learning Engineer to help build the causal ML foundation behind how DoorDash grows New Verticals. This is not a generic ML role with some experimentation work on the side. We are looking for someone who has built or deeply worked on production causal systems: uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation platforms, counterfactual policy evaluation, promotion optimization, or marketplace decisioning systems.

You will join a small, senior pod of causal ML and econometrics experts working across ML, Analytics, Product, and Engineering. The mandate is to build the causal spine for a large-scale consumer marketplace.

You're Excited About This Opportunity Because You Will…

• Design, build, and productionize causal ML systems that influence real marketplace decisions across New Verticals.
• Build uplift / heterogeneous treatment effect models for consumer lifecycle value, promotions, retention, and reactivation.
• Develop counterfactual evaluation frameworks for ranking, recommendations, search, promotions, substitutions, and marketplace interventions.
• Build systems that connect experimentation, observational data, and ML decisioning so teams can make better tradeoffs when randomized experiments are slow, noisy, or incomplete.
• Design surrogate metrics and early indicators that help teams move faster while preserving long-term marketplace health.
• Partner with econometrics and analytics leaders to choose the right methods: doubly robust estimation, IV, diff-in-diff, synthetic controls, double ML, CUPED-style variance reduction, contextual bandits, off-policy evaluation, and related approaches.
• Translate causal models into production systems that can shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.
• Raise the bar for causal reasoning across ML teams: when to trust a model, when not to, and how to debug causal claims in a real marketplace.

We're Excited About You Because You Have…

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Experience shipping models or decision systems in production, ideally in consumer marketplaces, ads, recommendations, search, pricing, promotions, logistics, fintech, or other high-scale settings.
• Strong judgment around the tradeoffs between randomized experiments, observational estimation, and model-based decisioning.
• Comfort debating and applying methods such as doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation.
• Strong ML engineering ability: you can build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to put them into production.
• Strong product judgment: you can connect methods to business decisions, not just optimize offline metrics.
• The ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.

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.
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.