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Present and critique an airline delay analysis

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's skills in time-series feature engineering and seasonality handling, data joining and leakage detection, hierarchical effects at route and airport levels, model selection (regression vs classification), metric choice, time-aware cross-validation, visualization critique, and experiment/KPI design.

  • hard
  • Capital One
  • Analytics & Experimentation
  • Data Scientist

Present and critique an airline delay analysis

Company: Capital One

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

Role play: you have 15 minutes to review a slide deck on predicting airline departure delays and then 20 minutes to present to the Director of Operations. The current analysis uses linear regression with date encoded as an integer and includes plots that mix weekdays and months without seasonality controls. Tasks: (1) Critique the feature engineering (e.g., date as categorical or cyclical features, weather joins, route-level effects, leakage risks such as using wheels-off time). (2) Propose a modeling approach (regression vs. classification on delay > 15 minutes), metrics (RMSE/MAE or AUC-PR/cost), and cross-validation that respects time order. (3) Explain two charts you would replace, how, and why; produce a clear narrative for a non-technical audience linking insights to actions (e.g., crew buffers by route and time-of-day). (4) Deliver a concrete recommendation that would reduce compensation payouts by 5% without increasing cancellations, and define the experiment or KPI plan to verify impact within four weeks.

Quick Answer: This question evaluates a data scientist's skills in time-series feature engineering and seasonality handling, data joining and leakage detection, hierarchical effects at route and airport levels, model selection (regression vs classification), metric choice, time-aware cross-validation, visualization critique, and experiment/KPI design.

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Capital One
Oct 13, 2025, 9:49 PM
Data Scientist
Technical Screen
Analytics & Experimentation
12
0

Predicting Airline Departure Delays — Technical Screen Prompt

Context

You have 15 minutes to review a slide deck on predicting airline departure delays, followed by a 20-minute presentation to the Director of Operations. The current analysis:

  • Uses linear regression with the date encoded as an integer.
  • Includes plots that mix weekdays and months without controlling for seasonality.

Assume you have historical flight-level data (schedules, realized times), route metadata, and access to weather data. The goal is to improve predictive accuracy and translate insights into operational actions.

Tasks

  1. Critique the current feature engineering, including:
    • How to encode time (date as categorical or cyclical features).
    • Weather joins and availability.
    • Route- and airport-level effects.
    • Leakage risks (e.g., using wheels-off time).
  2. Propose a modeling approach, covering:
    • Whether to predict continuous delay (regression) or classify delay > 15 minutes (or both).
    • Appropriate metrics (e.g., RMSE/MAE for regression; AUC-PR/cost-sensitive metrics for classification).
    • Cross-validation that respects time order.
  3. Identify two charts to replace from the current deck, explain the replacements (how and why), and provide a concise narrative for a non-technical audience that links insights to actions (e.g., crew buffers by route and time-of-day).
  4. Deliver a concrete recommendation to reduce compensation payouts by 5% without increasing cancellations, and define an experiment or KPI plan to verify impact within four weeks.

Solution

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