Staff Machine Learning Scientist, Applied Causal Inference

Location: San Francisco, CA, Sunnyvale, CA, Los Angeles, CA, Seattle, WA, New York City, NY | Type: Full-time | Posted: 8/19/2026

Compensation: $203,500 - $299,300 per year

About the role

The Causal Machine Learning Scientist role at DoorDash focuses on building causal decisioning systems for New Verticals such as grocery and retail. The team consists of experts in causal ML and econometrics, aiming to create a robust causal foundation for a large-scale consumer marketplace. The position emphasizes the development of production causal systems that influence marketplace decisions and improve tradeoffs in experimentation and observational data. Candidates will work closely with various teams to enhance causal reasoning and decision-making processes.

Requirements

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Experience shipping models or decision systems in production, ideally in consumer marketplaces or 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, and uplift modeling.
• Strong ML engineering ability to build reliable pipelines and evaluate models rigorously.
• Strong product judgment to connect methods to business decisions.

Responsibilities

• Design, build, and productionize causal ML systems that influence real marketplace decisions.
• Build uplift and heterogeneous treatment effect models for consumer lifecycle value and promotions.
• Develop counterfactual evaluation frameworks for various marketplace interventions.
• Build systems that connect experimentation, observational data, and ML decisioning.
• Design surrogate metrics and early indicators to help teams move faster.
• Partner with econometrics and analytics leaders to choose appropriate methods.
• Translate causal models into production systems that shape decisions in various areas.
• Raise the bar for causal reasoning across ML teams.