Logistics & Marketplace Dynamics Topics
Covers logistics management, supply chain operations, fulfillment, inventory optimization, carrier selection, distribution strategies, and marketplace dynamics including platform-based marketplaces, seller/buyer interactions, pricing, demand forecasting, competition, and marketplace optimization. This category also addresses cross-functional implications for product, operations, and business strategy in both physical and digital marketplace contexts.
DoorDash Domain Knowledge & Interest
Domain knowledge about DoorDash as an on-demand delivery marketplace, including how the platform handles order routing, courier (delivery partner) management, merchant/restaurant relationships, pricing and incentives, demand forecasting, fulfillment operations, and competitive positioning. Covers product and operations considerations unique to DoorDash, regulatory/compliance aspects, and strategic implications for marketplace design, growth, and user experience.
Marketplace and Multi Stakeholder Considerations
This topic assesses understanding of multi sided platform dynamics and how machine learning decisions affect multiple stakeholder groups simultaneously. Candidates should be able to describe how to balance competing objectives for customers, couriers or drivers, and merchants, how optimizations on one side can create negative externalities on another, and how to design metrics and experiments that surface cross side effects. Discussion should include incentive alignment, pricing and promotion effects, simulation or microsimulation approaches, fairness signals, guardrails, and long term platform health considerations. Interviewers look for evidence of anticipating gaming or feedback loops and proposing measurement and mitigation strategies.
Marketplace Matching and Routing
Study algorithmic and systems approaches for matching supply and demand and solving routing problems in multi sided marketplaces. Candidates should be able to formalize matching and dispatch problems using assignment, bipartite and multipartite matching, min cost flow, and vehicle routing formulations; reason about online and batch solutions; design approximation algorithms, greedy heuristics, and scalable distributed solvers; handle constraints such as time windows, capacity, batching, pooling, and fairness; consider incentive and pricing interactions with routing and allocation; evaluate solutions on metrics such as wait time, fill rate, throughput, and operational cost; and discuss simulation and offline evaluation strategies as well as integration with real time serving and monitoring.