Amazon·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026Remote

Summary

Amazon data science loop, one question but it was a monster. They gave you a three-way vendor decision and asked you to build basically an entire decision science framework from scratch, live. Walked out not totally sure if I nailed it or completely missed the point.

Questions Asked (1)

Your company is choosing between three employee training options: a standard vendor package, a premium vendor package, and an internal build. Design a full quantitative decision framework covering: how you'd define and causally estimate KPIs like completion rate and performance uplift; a risk-adjusted NPV model that accounts for implementation time, costs, benefits, data breach risk, and value lost during rollout; a sensitivity analysis with stopping rules and a pilot plan to update your estimates; and finally, given only ordinal information about the options, which would you pick and what would make you switch?

A/B Testing & ExperimentationProduct Analytics & MetricsTechnical Trade-offs

Author's notes

This is one of those questions where you realize halfway through your answer that you've been talking for five minutes and haven't gotten to the actual decision rule yet.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a decision framework: first define measurable KPIs and how to causally estimate them, then build a risk-adjusted NPV model with sensitivity analysis and stopping rules, and finally apply the framework to the ordinal choice. Emphasize iterative learning through a pilot and updating estimates, and justify your pick with clear switching conditions.

Pro tip: Anchor your framework in Amazon's leadership principles like 'Customer Obsession' and 'Bias for Action' by tying KPIs to customer impact and proposing a pilot to reduce uncertainty quickly. Also, explicitly state that the internal build option may offer strategic advantages like data control and customization, which can outweigh short-term costs.

1. Define KPIs and Causal Estimation

Identify key metrics such as completion rate, performance uplift, time-to-proficiency, and employee satisfaction. For causal estimation, propose randomized controlled trials (A/B tests) with proper power analysis, or quasi-experimental methods like difference-in-differences if randomization is infeasible.

2. Build Risk-Adjusted NPV Model

Estimate costs (implementation, licensing, maintenance), benefits (productivity gains, reduced turnover), and risks (data breach probability and impact, rollout delays). Discount cash flows using an appropriate rate, and incorporate risk by adjusting probabilities or using certainty equivalents.

3. Conduct Sensitivity Analysis and Define Stopping Rules

Perform Monte Carlo simulation or scenario analysis on key assumptions (e.g., completion rate, breach cost). Establish stopping rules: if pilot results show completion rate below X% or breach risk above Y%, halt rollout and reassess.

4. Design Pilot Plan to Update Estimates

Propose a small-scale pilot with a control group to measure KPIs and gather data on costs and risks. Use Bayesian updating or sequential testing to refine the NPV model and decide whether to proceed, pivot, or stop.

5. Make Ordinal Choice and Define Switching Conditions

Given only ordinal rankings (e.g., premium > standard > internal on quality, but internal > standard > premium on cost/control), choose the option that best aligns with strategic priorities. Specify what new information (e.g., pilot results showing internal build meets performance thresholds) would cause you to switch.

Key Points to Mention

Use randomized controlled trials (A/B tests) to causally estimate KPIs like completion rate and performance uplift, ensuring adequate sample size and avoiding common pitfalls like selection bias.
In NPV, account for implementation time by discounting cash flows appropriately, and include risk-adjusted probabilities for data breaches and rollout delays.
Sensitivity analysis should identify which assumptions most affect NPV; use tornado diagrams or Monte Carlo simulation to quantify uncertainty.
Stopping rules should be pre-defined with clear thresholds (e.g., if pilot completion rate < 70%, stop) to avoid escalation of commitment.
Pilot plan should be iterative: start small, measure, and update priors; consider Bayesian methods to combine prior knowledge with pilot data.
When choosing under ordinal information, weigh strategic factors like data control, customization, and long-term flexibility; internal build may be preferable if it aligns with core competencies and risk tolerance.

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