Propose a rigorous plan to prove an automated package-allocation system outperforms the manual baseline. Address interference and spillovers by choosing a randomization unit (e.g., station-hour switchback or courier-level cluster randomization) and justify it. Define primary/guardrail metrics (on-time rate, SLA breaches, courier overtime, customer contacts, fairness Gini), pre-specify analysis (intention-to-treat, CUPED/covariate adjustment, heterogeneity by zip density), and a power/MDE calculation (baseline on-time 92%, 100k orders/day, intra-cluster correlation assumptions). Include ramp/rollback criteria, duration, and a diff-in-diff fallback if perfect randomization isn’t feasible. Explain how you’ll prevent gaming and contamination (inventory locking, shadow assignments), and how you’ll conclude superiority with statistical and practical significance (e.g., uplift ≥0.6 pp with non-inferior guardrails).

This question evaluates skills in experimental design, causal inference, statistical power/MDE calculations, metric definition, and operational governance for large‑scale randomized trials in logistics.

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What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Amazon.

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This question is commonly asked for Data Scientist candidates at Amazon during technical interviews.

Prove new allocation outperforms manual baseline

Prove an Automated Package-Allocation System Outperforms Manual Baseline

Context

You work in a large last‑mile logistics network evaluating a new automated package‑allocation (dispatch) system versus the current manual process. Daily volume is ~100,000 orders. The baseline on‑time delivery rate is 92%.

Task

Design a rigorous experiment and analysis plan that:

Addresses interference and spillovers
- Propose and justify a randomization unit (e.g., station‑hour switchback or courier‑level cluster randomization).
- Explain operational controls to limit contamination.
Defines metrics and decision criteria
- Primary: on‑time delivery rate.
- Guardrails: SLA breaches, courier overtime, customer contacts, fairness (Gini of work allocation).
- Set superiority and non‑inferiority thresholds (e.g., uplift ≥ 0.6 pp for primary, non‑inferior guardrails).
Pre‑specifies the analysis
- Intention‑to‑treat (ITT) as primary; note any per‑protocol sensitivity.
- Variance reduction (CUPED/covariate adjustment).
- Heterogeneity by zip‑code density (urban vs suburban/rural).
- Inference approach and handling of clustering.
Includes power/MDE calculations
- Use baseline on‑time = 92%, 100k orders/day.
- State intra‑cluster correlation assumptions.
- Provide duration estimates to detect a 0.6 pp uplift.
Covers operations and governance
- Ramp plan, monitoring, and rollback criteria.
- Diff‑in‑diff fallback if perfect randomization isn’t feasible.
- Anti‑gaming/contamination controls (e.g., inventory locking, shadow assignments).

Task

Design a rigorous experiment and analysis plan that:

Addresses interference and spillovers

Propose and justify a randomization unit (e.g., station‑hour switchback or courier‑level cluster randomization).
Explain operational controls to limit contamination.

Defines metrics and decision criteria

Primary: on‑time delivery rate.
Guardrails: SLA breaches, courier overtime, customer contacts, fairness (Gini of work allocation).
Set superiority and non‑inferiority thresholds (e.g., uplift ≥ 0.6 pp for primary, non‑inferior guardrails).

Pre‑specifies the analysis

Intention‑to‑treat (ITT) as primary; note any per‑protocol sensitivity.
Variance reduction (CUPED/covariate adjustment).
Heterogeneity by zip‑code density (urban vs suburban/rural).
Inference approach and handling of clustering.

Includes power/MDE calculations

Use baseline on‑time = 92%, 100k orders/day.
State intra‑cluster correlation assumptions.
Provide duration estimates to detect a 0.6 pp uplift.

Covers operations and governance

Ramp plan, monitoring, and rollback criteria.
Diff‑in‑diff fallback if perfect randomization isn’t feasible.
Anti‑gaming/contamination controls (e.g., inventory locking, shadow assignments).

Prove new allocation outperforms manual baseline

Quick Overview

Prove an Automated Package-Allocation System Outperforms Manual Baseline

Context

Task

Solution

Comments (0)

Prove new allocation outperforms manual baseline

Quick Overview

Prove an Automated Package-Allocation System Outperforms Manual Baseline

Context

Task

Solution

Comments (0)