Staff Causal ML Scientist: Production Uplift & Decisioning

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

Job summary

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML foundation behind growth in New Verticals. You will work with a senior team to productionize uplift models, counterfactual evaluation, and decisioning systems in a high-scale marketplace.

You’ll translate causal models into production pipelines, collaborate with economists and data scientists, and help drive decisions in ranking, promotions, and inventory-aware discovery.

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 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: you can build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to put them into production.
• Strong product judgment: you can connect methods to business decisions, not just offline metrics.
• Ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.

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 for better tradeoffs when randomized experiments are slow or noisy.
• Design surrogate metrics and early indicators to move faster while preserving long-term health.
• Translate causal models into production systems that shape decisions in ranking, targeting, and budget allocation.
• Raise the bar for causal reasoning across ML teams: when to trust a model and how to debug causal claims in a real marketplace.

Skills

Causal inference

Econometrics

Experimentation

Causal ML

Production ML

Tools

IV

Diff-in-Diff

CUPED

Contextual bandits

Off-policy evaluation

Job description

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML foundation behind growth in New Verticals. You will work with a senior team to productionize uplift models, counterfactual evaluation, and decisioning systems in a high-scale marketplace.

You’ll translate causal models into production pipelines, collaborate with economists and data scientists, and help drive decisions in ranking, promotions, and inventory-aware discovery.