Staff Causal ML Scientist - Production Uplift & Evaluation

DoorDash USA; United States; Remote; USD 204,000 - 299,000; Full time

Benefits offered by this job

401(k) plan

Paid parental leave

Wellness benefits

Commuter benefits

Paid time off

Medical/dental/vision benefits

Job summary

DoorDash is hiring a Causal Machine Learning Engineer to build production causal systems powering decisions across New Verticals. The role focuses on uplift models, counterfactual evaluation, and connecting experimentation with ML decisioning in a high‑scale marketplace.

You will join a senior causal ML and econometrics pod, translating causal models into production scoring, ranking, and allocation decisions while collaborating with ML engineers, data scientists, economists and product partners.

Qualifications

• 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 the tradeoffs between randomized experiments, observational estimation, and model‑based 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 to help teams trade off when experiments are slow or noisy.
• Design surrogate metrics and early indicators to move faster while preserving long‑term health.

Skills

Causal inference

Econometrics

Experimentation

ML engineering

Job description

DoorDash is hiring a Causal Machine Learning Engineer to build production causal systems powering decisions across New Verticals. The role focuses on uplift models, counterfactual evaluation, and connecting experimentation with ML decisioning in a high‑scale marketplace.

You will join a senior causal ML and econometrics pod, translating causal models into production scoring, ranking, and allocation decisions while collaborating with ML engineers, data scientists, economists and product partners.