Explain grocery-specific product strategy and scrappy XP
Overview
This question evaluates a data scientist's product-strategy and experimentation design competencies, including operational constraints, metric definition, hypothesis framing, risk identification, and cross-functional leadership during a grocery vertical launch.
Uber
Oct 13, 2025, 9:49 PM
Data Scientist
Onsite
Behavioral & Leadership
0
0
Launching Grocery Delivery in NYC: Product and Experimentation Plan
Context
A delivery platform that is strong in restaurant delivery is launching a grocery vertical in NYC. Groceries differ operationally, economically, and behaviorally from restaurants, which must be reflected in product design and experimentation strategy.
Task
- Identify at least five aspects where grocery delivery is meaningfully different from restaurant delivery. For each, briefly note implications for product and for experimentation.
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Choose one growth bet and one efficiency bet. For each bet:
- State the hypothesis and target segment.
- Define crisp success metrics and leading indicators (with guardrails).
- Propose a scrappy, low-risk experiment: treatment, control, randomization unit, assignment, and duration.
- List the top three risks for your plan and propose mitigations.
- Explain how experimentation feasibility differs from restaurants (e.g., store-level randomization, switchbacks within store hours, catalog volatility).
- List the product and operational knowledge you would want beforehand to de-risk the launch and experiments.
Solution
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