Staff Machine Learning Scientist, Applied Causal Inference

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.…