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# Staff Machine Learning Scientist, Applied Causal Inference

- **Company:** DOORDASH, INC.
- **Location:** Los Angeles, CA, United States (Remote available)
- **Salary:** $203,500.0 - $299,300.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Software Debugging, Machine Learning, Build Tools, Machine Learning Operations
- **Published:** November 5, 2026
- **Apply:** https://www.careerbuilder.com/job-details/staff-machine-learning-scientist-applied-causal-inference-los-angeles-ca--b64f0ed8-86e8-4360-bc01-8ff921645e19

## About the Role

Machine Learning, Retail, Machine Learning, Policy Evaluation, Metrics, Production Systems, Econometrics, Product Engineering, Budgeting, Debugging Skills, * 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.
 * 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 optimize offline metrics.
 * The ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.

## Description

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.

You''re excited about this opportunity because you will…

 * 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.
 * 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, double ML, CUPED-style variance reduction, contextual bandits, off-policy evaluation, and related approaches.
 * Translate causal models into production systems that can shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.
 * Raise the bar for causal reasoning across ML teams: when to trust a model, when not to, and how to debug causal claims in a real marketplace.