Staff Machine Learning Scientist, Applied Causal Inference

Quick Facts

• Role: Causal Machine Learning Engineer

Description

DoorDash is building the next generation of causal decisioning systems for new verticals (grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories). The role focuses on building the causal ML foundation behind how DoorDash grows new verticals by developing production causal systems such as uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation platforms, counterfactual policy evaluation, promotion optimization, and marketplace decisioning.

Responsibilities

• 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 methods including doubly robust estimation, IV, diff-in-diff, synthetic controls, double ML, CUPED-style variance reduction, contextual bandits, and off-policy evaluation.

• Translate causal models into production systems that shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.

• Raise the bar for causal reasoning across ML teams by defining when to trust models, when not to, and how to debug causal claims.

Requirements

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.

• Experience shipping models or decision systems in production, ideally in high-scale domains such as consumer marketplaces, ads, recommendations, search, pricing, promotions, logistics, or fintech.

• Strong judgment around tradeoffs between randomized experiments, observational estimation, and model-based decisioning.

• Comfort debating and applying doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation.

• Strong ML engineering ability to build reliable pipelines, train models, and evaluate rigorously for production deployment.

• Strong product judgment connecting causal methods to business decisions.

• Ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.

Benefits

• Comprehensive benefits package including 401(k) with employer matching, 16 weeks paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave, medical/dental/vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program.

• Paid time off: flexible paid time off/vacation plus 80 hours paid sick time per year for salaried roles; hourly roles have PTO and sick time accrual rules.

• Base salary range (US): $203,500—$299,300 USD (localized by work location) and opportunities for equity grants.