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

Model waiting-time abandonment via survival

Quick Overview

This question evaluates proficiency in survival analysis and time-to-event modeling, including defining survival and hazard functions, handling right-censoring and left-truncation, encoding time-varying covariates, and interpreting hazard ratios from Cox PH versus Weibull AFT models.

Survival Modeling of Rider Abandonment During Pickup Waits

Context

You are modeling when a rider cancels (abandons) while waiting for pickup. Let time origin t = 0 be the ride request time. The event of interest is rider cancellation before pickup. If the rider is picked up before canceling, the observation is right-censored at pickup time.

Assume you have covariates including quoted ETA, surge multiplier, time-of-day, and location. Some covariates (e.g., quoted ETA, surge) may change over time.

Tasks

Define survival S(t) and hazard h(t) in this setting and relate them.
List key covariates and how to encode them (quoted ETA, surge multiplier, time-of-day, location).
Explain how to handle:
- Right-censoring (completed rides/pickups)
- Left-truncation (delayed entry, e.g., analysis begins at driver assignment)
- Time-varying covariates
Specify and compare a Cox proportional-hazards (PH) model vs. a Weibull accelerated failure time (AFT) model. Interpret hazard ratios for the covariates.
Compute the median additional wait a rider will tolerate under median covariates, for both models.
Describe diagnostics for PH assumptions and how violations would change your specification.

Quick Overview

Context

Assume you have covariates including quoted ETA, surge multiplier, time-of-day, and location. Some covariates (e.g., quoted ETA, surge) may change over time.

Tasks

Define survival S(t) and hazard h(t) in this setting and relate them.

List key covariates and how to encode them (quoted ETA, surge multiplier, time-of-day, location).

Explain how to handle:

Right-censoring (completed rides/pickups)
Left-truncation (delayed entry, e.g., analysis begins at driver assignment)
Time-varying covariates

Specify and compare a Cox proportional-hazards (PH) model vs. a Weibull accelerated failure time (AFT) model. Interpret hazard ratios for the covariates.

Compute the median additional wait a rider will tolerate under median covariates, for both models.

Describe diagnostics for PH assumptions and how violations would change your specification.

Model waiting-time abandonment via survival

Quick Overview

Survival Modeling of Rider Abandonment During Pickup Waits

Context

Tasks

Solution

Comments (0)

Model waiting-time abandonment via survival

Quick Overview

Survival Modeling of Rider Abandonment During Pickup Waits

Context

Tasks

Solution

Comments (0)