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DoorDash·Data Scientist·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

DoorDash data science interview with a meaty product analytics case about a mobile app pop-up tanking partner-channel conversions. One question but it had a lot of moving parts and I felt like I was chasing my tail a bit trying to structure it on the fly.

Questions Asked (1)

Q1

After a new mobile app download pop-up was added, first-time purchases from the partner-referral channel dropped sharply. How would you confirm the pop-up caused this, quantify the trade-off between app installs gained and orders lost, and design an experiment to decide whether to keep, modify, or remove it?

A/B Testing & ExperimentationProduct Analytics & MetricsRoot Cause Analysis
Author's notes

I started with the funnel mapping which felt right, but I got a bit tangled when they pushed on how to separate the pop-up effect from seasonality and partner mix shifts.

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AI HintsAI Generated

Suggested Approach

Start by confirming causality through a quasi-experimental design like difference-in-differences or synthetic control, comparing partner-referral channels to unaffected channels before and after the pop-up launch. Then quantify the trade-off by estimating incremental app installs and the lost orders, converting both to a common metric like profit or LTV. Finally, design an A/B test with variants (keep, modify, remove) to measure net impact and decide based on statistical significance and business thresholds.

Pro tip: Always consider potential confounders like seasonality or concurrent marketing campaigns, and use a holdout group if possible to isolate the pop-up's effect. Also, think about long-term effects: the pop-up might deter high-value users, so measure impact on retention and LTV, not just immediate orders.

1. Confirm Causality

Use a difference-in-differences or synthetic control approach comparing partner-referral channels to other channels before and after the pop-up launch. Check for parallel trends and rule out alternative explanations like seasonality or other simultaneous changes.

2. Quantify Trade-off

Estimate the number of incremental app installs attributable to the pop-up and the number of lost first-time purchases from the partner-referral channel. Convert both to a common metric such as expected profit or LTV to compare.

3. Design Experiment

Run an A/B test with three arms: keep pop-up, modify pop-up (e.g., different messaging or timing), and remove pop-up. Randomize at user level, ensure sufficient power, and measure both installs and purchases as primary metrics.

4. Analyze and Decide

Analyze the experiment results using appropriate statistical tests, considering both short-term and long-term metrics. Decide whether to keep, modify, or remove the pop-up based on net impact and business goals.

Key Points to Mention

  • Difference-in-differences or synthetic control to establish causality
  • Quantify trade-off using a common metric like profit or LTV
  • A/B test with multiple variants (keep, modify, remove)
  • Consideration of confounders and seasonality
  • Long-term impact on retention and LTV
  • Statistical power and significance in experiment design

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