Causal ML Engineer: Production Uplift & Experimentation

DoorDash; New York (NY); On-site; USD 204,000 - 299,000; Full time

Benefits offered by this job

401(k) with employer matching

Paid parental leave (16 weeks)

Wellness benefits

Commuter benefits match

Paid time off

Paid sick leave

Medical, dental, and vision insurance

11 paid holidays

Disability insurance

Life insurance

Family-forming assistance

Mental health program

Job summary

DoorDash is seeking a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. You will join a small, senior pod of causal ML and econometrics experts working across ML, Analytics, Product, and Engineering to design production systems that influence ranking, recommendations, and marketplace decisions.

You will develop uplift models, counterfactual evaluation frameworks, and surrogate metrics, connecting experimentation, observational data, and

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.
• 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.

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 so teams can make better tradeoffs when randomized experiments are slow, noisy, or incomplete.

Skills

Causal inference

Econometrics

Experimentation

Production ML systems

ML engineering

Product judgment

Job description

DoorDash is seeking a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. You will join a small, senior pod of causal ML and econometrics experts working across ML, Analytics, Product, and Engineering to design production systems that influence ranking, recommendations, and marketplace decisions.

You will develop uplift models, counterfactual evaluation frameworks, and surrogate metrics, connecting experimentation, observational data, and