Staff Machine Learning Scientist, Applied Causal Inference

<div class="content-intro"><p><img style="display: none; max-width: 100%;" src="https://click.appcast.io/greenhouse-te8/a31.png?ent=34&amp;e=22630&amp;t=1701374353806" width="1px"> <img style="display: none; max-width: 100%;" src="https://track.jobadx.com/v1/i.gif?utm_pixel=224e990b-8ff4-4287-8d5d-2ff09647f181&amp;utm_ptz=EST&amp;utm_rqt=track" alt="" width="1"></p></div><h2><strong>About the Team</strong></h2> <p>DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories. These businesses operate in high-dimensional, messy marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question.</p> <h2><strong>About the Role</strong></h2> <p>We are hiring a <strong>Causal Machine Learning Engineer</strong> 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.</p> <p>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.</p> <h2><strong>You're excited about this opportunity because you will…</strong></h2> <ul> <li>Design, build, and productionize causal ML systems that influence real marketplace decisions across New Verticals.</li> <li>Build <strong>uplift / heterogeneous treatment effect models</strong> for consumer lifecycle value, promotions, retention, and reactivation.</li> <li>Develop <strong>counterfactual evaluation frameworks</strong> for ranking, recommendations, search, promotions, substitutions, and marketplace interventions.</li> <li>Build systems that connect <strong>experimentation, observational data, and ML decisioning</strong> so teams can make better tradeoffs when randomized experiments are slow, noisy, or incomplete.</li> <li>Design <strong>surrogate metrics and early indicators</strong> that help teams move faster while preserving long-term marketplace health.</li> <li>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.</li> <li>Translate causal models into production systems that can shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.</li> <li>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.</li> </ul> <h2><strong>We're excited about you because you have…</strong></h2> <ul> <li>Deep practical experience with <strong>causal inference, econometrics, experimentation, or causal ML</strong>.</li> <li>Experience shipping models or decision systems in production, ideally in consumer marketplaces, ads, recommendations, search, pricing, promotions, logistics, fintech, or other high-scale settings.</li> <li>Strong judgment around the tradeoffs between randomized experiments, observational estimation, and model-based decisioning.</li> <li>Comfort debating and applying methods such as <strong>doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation</strong>.</li> <li>Strong ML engineering ability: you can build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to put them into production.</li> <li>Strong product judgment: you can connect methods to business decision