Staff Machine Learning Scientist, Applied Causal Inference
DoorDash USA · San Francisco, CA; Sunnyvale, CA; Los Angeles, CA; Seattle, WA; New York City, NY
About this role
**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’re 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’re looking for someone who has built or deeply worked on **production causal systems**, including: uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation platforms, counterfactual policy evaluation, promotion optimization, and marketplace decisioning systems. You’ll 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’ll be 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 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 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: build reliable pipelines, train models, evaluate rigorously, and partner with platform teams to put them into production. - Strong product judgment: connect methods to business decisions—not just optimize offline metrics. - Ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders. **Compensation & B
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