DoorDash's Supply Strategy and Optimization team needs to ensure the marketplace is balanced, efficient, and profitable by intelligently managing Dasher supply and engagement. This involves building systems to forecast supply needs, optimize incentive spend, and enable data-driven decisions for Dasher acquisition, retention, and mobilization, ultimately delivering an exceptional customer experience.
Requirements
- PhD or 2+ years of industry experience post graduate degree of developing advanced machine learning models with business impact.
- Hands-on experience owning production ML models and pipelines
- Strong fundamentals in applied machine learning, optimization, and experiment design
Responsibilities
- Design and deploy production ML systems that drive decision-making across Dasher acquisition, incentives, and marketplace balancing.
- Own the end-to-end ML lifecycle—from feature engineering and model training to deployment, experimentation, and monitoring.
- Causal inference modeling to measure the incremental impact of Dasher acquisition and incentive strategies.
- Incentive optimization frameworks that personalize pay structures and improve efficiency.
- Budget allocation and forecasting models that identify optimal spend across acquisition, referrals, and retention.
- Platformization of ML systems to standardize forecasting, monitoring, and experimentation at scale.
- Develop automated experimentation pipelines for evaluating incentive performance and marketplace interventions.
Other
- Work closely with partners in Product, Operations, and Analytics to shape how DoorDash optimizes supply and mobilization at scale.
- Collaborate cross-functionally to deliver scalable, production-grade ML solutions.
- Thrive in ambiguous, fast-paced environments and are motivated by measurable impact.
- Track record of collaborating with cross-functional partners and operating with end-to-end ownership.
- Passionate about using ML to solve high-impact, real-world problem.
