Diagnose Flight Delays and Burger Launch

Q: Diagnose Flight Delays and Burger Launch

This question evaluates a data scientist's skills in data auditing and cleaning, feature engineering and modeling decisions (regression versus classification), handling multicollinearity and hierarchical heterogeneity, interpreting low model fit and separating predictive from causal conclusions, and constructing financial break-even and sensitivity analyses for a product launch. Commonly asked in analytics & experimentation interviews, it tests statistical modeling, causal inference, and business-analytics reasoning across both conceptual understanding and practical application, requiring reasoning about noisy real-world data, modeling trade-offs, and quantitative business impact.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Q: What difficulty level is this interview question?

This is a easy difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Capital One.

Q: What role is this question designed for?

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

Question

Loading...

You are given two analytics case questions.

Part A: Flight delay analysis An airline wants to understand and predict delays. You receive a flight-level dataset with these columns:

flight_id STRING
airport_id STRING
aircraft_type STRING
seats INT, but some rows contain negative values
gate_attendants INT
ground_attendants INT
passenger_count INT
weather_score FLOAT
day_of_week STRING
delay_minutes FLOAT

Some rows have missing values. Exploratory charts suggest that delays are higher on Mondays and Fridays, more attendants are associated with lower delays, airports behave differently, and gate_attendants, ground_attendants, and passenger_count are highly correlated.

How would you:

audit and clean the data,
decide whether to model delay_minutes as a regression target or convert the problem to a binary is_delayed classification task,
handle multicollinearity and airport or aircraft heterogeneity,
interpret a low R-squared,
separate predictive conclusions from causal conclusions?

Part B: Vegan burger launch A restaurant chain is deciding whether to introduce a vegan burger. You are given a one-time launch cost, recurring fixed cost, vegan burger unit cost, vegan burger unit price, and the current unit economics of the regular burger. Management believes that after launch, the sales mix would be vegan : regular = 2 : 3.

Using the interviewer-provided numbers, the algebra implies that total burger volume would need to increase by about 60% to match current profit.

How would you:

set up the profit equation and break-even condition,
make an initial recommendation,
explain how your original decision could still have been reasonable if a competitor later launched a vegan burger successfully?

Diagnose Flight Delays and Burger Launch

Quick Overview

Solution

Comments (0)