Delta Air Lines Data Scientist Interview Questions + Guide in 2025
Overview
Getting ready for an Data Scientist interview at Delta Air Lines? The Delta Air Lines Data Scientist interview span across 10 to 12 different question topics. In preparing for the interview:
- Know what skills are necessary for Delta Air Lines Data Scientist roles.
- Gain insights into the Data Scientist interview process at Delta Air Lines.
- Practice real Delta Air Lines Data Scientist interview questions.
Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Delta Air Lines Data Scientist interview.
Challenge
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Delta Air Lines Data Scientist Interview Process
Typically, interviews at Delta Air Lines vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.
We've gathered this data from parsing thousands of interview experiences sourced from members.
Delta Air Lines Data Scientist Interview Questions
Practice for the Delta Air Lines Data Scientist interview with these recently asked interview questions.
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SQL | Easy | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Delta Airlines is analyzing how passengers travel across its domestic network. The analytics team wants to understand which routes are most commonly flown out of each airport, and how long these popular journeys usually take. You’re working as a data analyst at Delta Airlines, and you’re provided with a table Write a SQL query to return the top 5 most frequent flight routes for each origin, ordered first by frequency descending, and then (for ties) by average journey duration ascending. Notes:
Schema:Input:
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Column | Type |
|---|---|
| flight_id | INTEGER |
| origin | VARCHAR |
| destination | VARCHAR |
| departure_time | DATETIME |
| arrival_time | DATETIME |
Output:
| Column | Type |
|---|---|
| origin | VARCHAR |
| destination | VARCHAR |
| route_frequency | INTEGER |
| avg_duration_minutes | DATETIME |
Example:
Input Data
flights
| flight_id | origin | destination | departure_time | arrival_time |
|---|---|---|---|---|
| 1 | JFK | LAX | 2025-11-19 08:00 | 2025-11-19 11:30 |
| 2 | JFK | LAX | 2025-11-19 14:00 | 2025-11-19 17:40 |
| 3 | JFK | ORD | 2025-11-19 09:00 | 2025-11-19 10:45 |
| 4 | JFK | ORD | 2025-11-20 09:00 | 2025-11-20 10:42 |
| 5 | JFK | MIA | 2025-11-19 12:00 | 2025-11-19 15:00 |
| 6 | JFK | MIA | 2025-11-20 12:00 | 2025-11-20 14:55 |
| 7 | LAX | SFO | 2025-11-19 07:00 | 2025-11-19 08:25 |
| 8 | LAX | SFO | 2025-11-19 16:00 | 2025-11-19 17:20 |
| 9 | LAX | SEA | 2025-11-19 06:00 | 2025-11-19 08:40 |
| 10 | LAX | SEA | 2025-11-20 06:00 | 2025-11-20 08:35 |
Output
| origin | destination | route_frequency | avg_duration_minutes |
|---|---|---|---|
| JFK | LAX | 2 | 210 |
| JFK | ORD | 2 | 103 |
| JFK | MIA | 2 | 172 |
| LAX | SFO | 2 | 82 |
| LAX | SEA | 2 | 157 |
Explanation:
From each origin (JFK, LAX), the query returns the top 5 most frequent destination routes. Since each origin has only two routes in this dataset, they are all included. For routes with the same frequency, sorting is done by increasing average duration.
SQL | Easy | |
Machine Learning | Medium | |
Statistics | Medium | |
SQL | Hard | |
Machine Learning | Medium | |
Python | Easy | |
Deep Learning | Hard | |
SQL | Medium | |
Statistics | Easy | |
Machine Learning | Hard |
Discussion & Interview Experiences