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DoorDash·Software Engineer·Onsite - Product Sense / Strategy·Senior

SeniorPrefer not to say
May 2026

Summary

DoorDash ML/product case focused entirely on designing a promotions system end to end. It was one long open-ended question that kept branching the deeper we went, and I left feeling like I'd only scratched the surface of what they were actually looking for.

Questions Asked (1)

Q1

Design a system to send promotional offers (discounts, coupons, etc.) to drive consumer growth. Walk through goal setting, metrics, audience targeting, uplift modeling, budget constraints, A/B testing, and how you'd track long-term retention versus short-term redemption.

A/B Testing & ExperimentationProduct Analytics & MetricsProduct Strategy
Author's notes

This sprawled way more than I expected.

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AI HintsAI Generated

Suggested Approach

Structure your answer around a clear framework: start with goal setting and metrics, then move to audience targeting and uplift modeling, followed by budget constraints and A/B testing, and finally discuss long-term retention vs. short-term redemption. Emphasize the trade-offs and how you would measure success at each stage.

Pro tip: Highlight the importance of incrementality: use uplift modeling to target only those who would not convert without the offer, and design holdout groups to measure long-term effects. This shows you understand that not all redemptions are incremental and that retention matters more than short-term spikes.

1. Define Goals and Metrics

Clarify the primary objective (e.g., drive consumer growth) and define both short-term (redemption rate, incremental orders) and long-term (retention, LTV) metrics. Align these with business goals.

2. Audience Targeting and Uplift Modeling

Segment users based on behavior and use uplift modeling to identify persuadables—those who will only convert with the offer. Avoid targeting 'sure things' and 'lost causes' to maximize ROI.

3. Budget Constraints and Offer Design

Determine budget allocation across segments and design offers (discount depth, type) to maximize incremental profit under constraints. Consider marginal ROI and diminishing returns.

4. A/B Testing and Experimentation

Design randomized controlled trials with proper control groups to measure incremental impact. Include holdout groups to assess long-term effects and avoid confounding.

5. Measure Long-Term Retention vs. Short-Term Redemption

Track metrics like repeat purchase rate, churn, and LTV for treated vs. control over extended periods. Analyze whether short-term redemption leads to sustained behavior change or just subsidizes existing users.

Key Points to Mention

  • Incrementality and uplift modeling: targeting only those who would not convert without the offer.
  • A/B testing with control and holdout groups to measure true causal impact.
  • Budget optimization: allocating spend to segments with highest incremental ROI.
  • Long-term retention metrics: repeat purchase rate, churn, LTV, and cohort analysis.
  • Avoiding cannibalization: ensuring offers don't just subsidize users who would have purchased anyway.
  • Trade-offs between short-term redemption spikes and long-term customer value.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.