Staff Machine Learning Scientist, Applied Causal Inference

DoorDash connects customers with local businesses for delivery, from restaurants to grocery, retail and its own DashMart stores. 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.

Key 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
• 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

Requirements

• 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
• 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
• Build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to put them into production
• Connect methods to business decisions, not just optimize offline metrics

Location and schedule

• Onsite in San Francisco, California

Salary and benefits

US base salary $203,500 to $299,300 a year. DoorDash also offers benefits; the full list is on the posting.

How to apply

Apply on the company's own site.