Design a privacy-safe personalization system for Grocery

Problem Statement Description

Product context: DoorDash is a local commerce and delivery platform; its products include restaurant delivery, DashPass, grocery and retail delivery, merchant tools, and dasher tools.

DoorDash Grocery shoppers often need to rebuild a basket across recurring trips, discover relevant items from local merchants, and make substitutions when inventory changes. Personalization could improve this experience by making search, recommendations, reordering, promotions, and substitution flows feel more relevant, but grocery data can be sensitive because it may reveal household composition, health needs, religion, income signals, or personal routines.

In this technical PM exercise, you are asked to design a privacy-safe personalization system for DoorDash Grocery. Focus on how the product and underlying systems should support relevant shopping experiences while respecting user trust, merchant constraints, marketplace reliability, and privacy/security expectations. The scope should include both the shopper-facing experience and the technical/data foundations needed to power it responsibly.

Your answer should clarify what user problem you are solving, what data the system needs, how personalization decisions are made and served, and how privacy protections are built into the design. You should also consider how the system would operate in a real local commerce marketplace with changing inventory, multiple merchants, delivery constraints, and the need to balance shopper value with merchant ROI and operational reliability.

The experience should consider:

- Shopper workflows such as weekly basket building, search, browse, reorder, substitutions, deals, and checkout

- Data inputs and boundaries, including purchase history, browsing behavior, location, inventory, merchant catalog data, and explicit user preferences

- Privacy requirements such as consent, transparency, data minimization, retention, user controls, and handling of sensitive inferences

- System architecture needs, including APIs, feature stores or data pipelines, ranking services, real-time inventory signals, and experimentation support

- Reliability and freshness trade-offs when grocery availability, pricing, and delivery windows change frequently

- Security, access control, auditability, and compliance considerations for shopper-level behavioral data

- Rollout, observability, metrics, guardrails, and failure modes for a personalization system in production

The goal is to define a product and technical approach that makes DoorDash Grocery more useful and efficient for shoppers without compromising privacy or trust. Your response should demonstrate how you would translate a broad product need into clear requirements, responsible data usage, system design choices, rollout considerations, and measurable product outcomes.