This one tripped me up more than I expected.
Start by clarifying the program's goal and success metrics, then outline a structured evaluation plan that combines business impact, user experience, and statistical rigor. Emphasize the need for an A/B test to measure causal effects, while also considering guardrail metrics and long-term holdout groups.
Pro tip: Frame your answer around the trade-off between short-term gains and long-term marketplace health, showing you understand DoorDash's three-sided marketplace. Mention that you'd align with cross-functional partners (Product, Engineering, Ops) early to define metrics and avoid siloed analysis.
Ask clarifying questions to understand what Top-Dasher prioritization aims to achieve (e.g., reduce delivery time, increase Dasher retention) and how success is defined. Formulate clear hypotheses about expected impacts on key stakeholders.
Identify primary success metrics (e.g., delivery efficiency, Dasher satisfaction) and guardrail metrics (e.g., customer wait time, order cancellation rate) to ensure no unintended harm. Consider both short-term and long-term metrics.
Propose an A/B test with random assignment of Dashers or orders, ensuring sufficient sample size and power. Discuss potential interference between Dashers and how to mitigate it (e.g., cluster randomization).
Plan to analyze results using appropriate statistical methods (e.g., t-tests, regression) and check for heterogeneous treatment effects. Validate findings with robustness checks and sensitivity analyses.
Synthesize findings into a clear recommendation, weighing statistical significance, practical significance, and business impact. If positive, propose a phased rollout with continued monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the incentive's goal—likely increasing Dasher engagement (e.g., hours worked, deliveries completed)—and select primary metrics that directly reflect that goal, such as average deliveries per active Dasher per week. Then design a rigorous A/B test with a clear treatment (extra pay), control (no extra pay), sufficient sample size and duration to detect meaningful effects, and guardrails like delivery quality and cost per delivery.
Pro tip: Emphasize that the incentive might attract Dashers who would have been active anyway (selection effect), so consider measuring incremental engagement via a holdout group and analyzing heterogeneous treatment effects by Dasher tenure or prior activity level.
Identify primary metrics that directly measure engagement (e.g., deliveries per Dasher, active hours, retention) and secondary metrics like Dasher satisfaction. Ensure they are aligned with business goals and measurable.
Randomly assign Dashers to treatment (receive extra pay) and control (no extra pay). Ensure randomization is at the Dasher level and that groups are comparable via pre-experiment covariates.
Calculate required sample size using power analysis (e.g., 80% power, 5% significance) based on expected effect size and variance. Choose experiment length to capture full behavior cycles (e.g., 4-6 weeks) while avoiding novelty effects.
Monitor metrics that should not degrade, such as delivery time, customer ratings, and cost per delivery. Set thresholds for stopping the experiment if guardrails are violated.
Compare primary metrics between groups using appropriate statistical tests. Check for heterogeneous effects and ensure results are robust (e.g., via bootstrapping). Consider long-term impact and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Probably the most interesting question of the three.
Start by outlining the tradeoffs between per-order and per-hour pay from the perspectives of Dashers, customers, and the platform, focusing on incentives, cost, and marketplace efficiency. Then propose an A/B test with clear metrics for marketplace health, such as delivery times, acceptance rates, and retention, while addressing potential confounds and ethical considerations.
Pro tip: Emphasize that the best pay model may depend on market conditions (e.g., urban vs. rural) and that a one-size-fits-all approach is unlikely; suggest testing heterogeneous effects across markets.
Discuss how per-order pay incentivizes speed and volume, potentially leading to rushed deliveries and lower quality, while per-hour pay incentivizes availability and may reduce urgency, affecting delivery times and costs.
Select metrics that capture marketplace health, such as delivery time, order completion rate, Dasher acceptance rate, customer satisfaction, Dasher retention, and cost per delivery.
Propose a randomized controlled trial (A/B test) where Dashers are randomly assigned to per-order or per-hour pay, ensuring proper randomization, sample size, and duration to detect meaningful effects.
Compare metrics between groups using statistical tests, check for heterogeneous effects across markets or Dasher segments, and consider qualitative feedback.
Acknowledge potential confounds (e.g., Dasher selection bias, novelty effects), discuss ethical concerns, and suggest iterative testing or hybrid models if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.