Staff Machine Learning Scientist, Applied Causal Inference

DoorDash US
San Francisco, Sunnyvale, Los Angeles, Seattle, New York
Workplace: OnsiteFull timeUSD 203,500 - 299,300 annuallyFunction: Data Science & Machine LearningSkills: ["Product judgment","Collaboration","Debugging","Causal reasoning"]

Build and productionize causal machine learning systems that shape real marketplace decisions for new verticals. Develop uplift and heterogeneous treatment effect models, counterfactual evaluation frameworks, and surrogate metrics that connect experimentation and observational data to ML decisioning. Partner with econometrics and analytics leaders to apply methods like doubly robust estimation and diff-in-diff, translating causal models into ranking, targeting, and promotion optimization.

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DoorDash US
DoorDash US
1 day ago

Staff Machine Learning Scientist, Applied Causal Inference

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Last checked: 1 hour agoStatus: Live

Job Summary

Build and productionize causal machine learning systems that shape real marketplace decisions for new verticals. Develop uplift and heterogeneous treatment effect models, counterfactual evaluation frameworks, and surrogate metrics that connect experimentation and observational data to ML decisioning. Partner with econometrics and analytics leaders to apply methods like doubly robust estimation and diff-in-diff, translating causal models into ranking, targeting, and promotion optimization.
Location: San Francisco, Sunnyvale, Los Angeles, Seattle, New York
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Design, build, and productionize causal ML systems that influence real marketplace decisions across new verticals.
  • •Build uplift and 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.
  • •Connect experimentation, observational data, and ML decisioning to help teams make tradeoffs when experiments are slow, noisy, or incomplete.
  • •Translate causal models into production systems for ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.

Pay and Benefits

Salary: USD 203,500 - 299,300 annually
Equity and Bonus:Equity
Perks:401kPaid ParentalHealth InsuranceDentalVisionPaid Leave

Key Requirements

  • •Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
  • •Experience shipping causal models or decision systems in production, ideally in consumer marketplaces or other high-scale settings.
  • •Strong judgment around tradeoffs between randomized experiments, observational estimation, and model-based decisioning.
  • •Comfort 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 to build reliable pipelines, rigorously evaluate models, and partner to put them into production.
Experience:Causal inferenceEconometricsExperimentationConsumer marketplacesRecommendationsSearchPricingPromotions
Skills:Product judgmentCollaborationDebuggingCausal reasoning
Tech Stack:Uplift modelsHeterogeneous treatment effect modelsCounterfactual policy evaluationSurrogate metricsDoubly robust estimationIVDiff-in-diffSynthetic controlsDouble MLCUPED-style variance reductionContextual banditsOff-policy evaluationCausal ML systemsExperimentation platformsPromotion optimizationMarketplace decisioning

Company Brief

DoorDash US
On-demand logistics platform connecting consumers with local restaurants, grocers, and retailers for food delivery, pickup, and convenience services. Operates a marketplace and last-mile delivery network while offering tools and analytics for merchants and couriers.
Industry: Online Marketplaces
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Francisco, United States
Founded: 2013
WebsiteLinkedIn