Your ride-hailing company wants to measure 'driver experience.' Propose a quantitative Driver Experience Index (DEI) combining earnings stability, utilization, fairness, and product friction. Specify: (a) exact event-level metrics and the weighting/aggregation scheme, (b) how you would validate reliability and sensitivity of the DEI (e.g., test-retest, correlation with guardrails, sensitivity to known changes), (c) how to de-bias for driver mix (tenure, geography, vehicle type, shift) via stratification or reweighting, and (d) an experiment to estimate the causal impact of a new dispatch policy on DEI while avoiding seasonality and Simpson’s paradox; be precise about the randomization unit, expected effect size, and power.

This question evaluates a data scientist's competencies in designing composite metrics, event-level aggregation, statistical validation, debiasing for sample mix, and causal experiment design to measure policy impacts.

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This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Uber.

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

Measure driver experience quantitatively

Driver Experience Index (DEI) — Design, Validation, Debiasing, and Experimentation

Context: You are a Data Scientist at a ride-hailing company. Define a quantitative Driver Experience Index (DEI) that combines four pillars of driver experience: earnings stability, utilization, fairness, and product friction. Be precise about event-level definitions, aggregation, validation, debiasing, and experimentation.

Requirements:

(a) Specify exact event-level metrics for each pillar and the weighting/aggregation scheme into a single DEI score.

(b) Describe how you would validate the reliability and sensitivity of the DEI (e.g., test–retest reliability, correlation with guardrail outcomes, responsiveness to known changes).

(d) Propose an experiment to estimate the causal impact of a new dispatch policy on DEI while avoiding seasonality and Simpson’s paradox. Be precise about the randomization unit, expected effect size, and power.

Driver Experience Index (DEI) — Design, Validation, Debiasing, and Experimentation

Requirements:

(a) Specify exact event-level metrics for each pillar and the weighting/aggregation scheme into a single DEI score.

(b) Describe how you would validate the reliability and sensitivity of the DEI (e.g., test–retest reliability, correlation with guardrail outcomes, responsiveness to known changes).

Measure driver experience quantitatively

Quick Overview

Driver Experience Index (DEI) — Design, Validation, Debiasing, and Experimentation

Solution

Submit Your Answer to Earn 20XP

Measure driver experience quantitatively

Quick Overview

Driver Experience Index (DEI) — Design, Validation, Debiasing, and Experimentation

Solution

Submit Your Answer to Earn 20XP