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Hawaiian airlines Interview Questions

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Machine Learning

85% vs 82%

Classification and Regression

Build a k Nearest Neighbors classification model from scratch.

KNN From Scratch

Matching Siblings

Bomb Detection

Business Case

Declining Applicants

Game Feature Home

How to boost presence in high-demand city areas

Incentive Scheme

Meta in an Emerging Market

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

How would you design a data warehouse for a e-commerce company looking to expand internationally?

International e-Commerce Warehouse

Find top frequent flights between source and destination.

Flight Routes - 2

Analytics

How would you determine customer service quality through a chat box?

Measuring Customer Service Quality

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

Let’s say that you’re looking at the metrics of a job board. You see that the number of job postings per day has more or less remained the same the last few months but the number of applicants that have applied to jobs has steadily been decreasing.

Why would this be happening?

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

Statistics

Build a \(k\) Nearest Neighbors classification model from scratch with the following conditions:

<ul>
<li>Use Euclidian distance (aka, the “2 norm”) as your closeness metric</li>
<li>Your function should be able to handle data frames of arbitrary many rows and columns</li>
<li>If there is a tie in the class of the \(k\) nearest neighbors, rerun the search using \(k-1\) neighbors instead</li>
<li>You may use <code>pandas</code> and <code>numpy</code> but NOT <code>scikit-learn</code></li>
</ul>

Example:

Input:

<pre tabindex="0" class="chroma"><code>k = 5
new_point = [0.5,-2,8]
print(data)
...
 Var1 Var2 Var3 Target
0 -3.279536 3.362223 2.847892 2
1 -0.791565 1.742475 2.151587 2
2 -0.785992 -0.938681 -0.459770 0
3 -1.068190 1.461051 0.127130 3
4 -0.367568 -0.870240 -0.225734 0
.. ... ... ... ...
95 -1.327175 1.971085 -0.690689 2
96 -3.203714 1.847649 0.778901 2
97 -0.587640 0.647458 2.094385 2
98 0.363644 -0.509795 2.514191 1
99 -0.673498 2.955285 2.102122 4

[100 rows x 4 columns]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def kNN(k, new_point, data) -&gt; 2
</code></pre>

What do you think the distribution of time spent per day on Facebook looks like? What metrics would you use to describe that distribution?

Time on FB Distribution

Let’s say that you work on the revenue forecasting team at a company like Facebook.

An executive comes to you asking about how much revenue Facebook will make in the coming year.

How would you forecast revenue for the next year?

Forecasting New Year Revenue

Forecasting & Time Series

Write a function <code>knn</code> which returns the nearest data point from a list of data points to a given query point. Use Euclidean distance as the similarity measure. For the purpose of this task, consider the scenario where \( k=1 \), meaning you only need to find the single closest data point.

Note: Using external libraries such as numpy and scikit-learn is not allowed.

Example:

Input:

<pre tabindex="0" class="chroma"><code>data_points = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]

querying_point = [1, 9, 7]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def knn(data_point: List[List[float]], query_point: List[float]) -&gt; [4, 5, 6] 
</code></pre>

Where k=1, write a KNN algorithm from scratch.

A Simpler KNN From Scratch

Describe a data project you worked on. What were some of the challenges you faced?

We have two models: one with 85% accuracy, one 82%. Which one do you pick?

Let’s say you work for an e-commerce company. Vendors can send products to the company’s warehouse to be listed on the website. Users are able to order any in-stock products and submit returns for refunds if they’re not satisfied.

The front end of the website includes a vendor portal that provides sales data in daily, weekly, monthly, quarterly, and yearly intervals.

The company wants to expand worldwide. They put you in charge of designing its end-to-end architecture, so you need to know what significant factors you’ll need to consider. What clarifying questions would you ask?

What kind of end-to-end architecture would you design for this company (both for ETL and reporting)?

What are the key differences between classification models and regression models?

What is the MOST important difference between regression and classification models?

What is the MOST important difference between regression and classification models?

Classification vs Regression

You work as an ML engineer for a large company. The company wants to implement a machine learning system that utilizes facial recognition to facilitate employee clock-in, clock-out, and access to secure systems. The company also hires temporary contract consultants who need to be able to use the system.

How would you design this system?

Designing a secure and user-friendly facial recognition system for employee management while prioritizing privacy and ethical considerations

Distributed Authentication Model

Concurrent & Distributed Systems

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 <code>flights</code> where each row represents one scheduled flight.

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:

<ol>
<li>A route is defined as a unique <code>(origin, destination)</code> pair.</li>
<li>Assume there is no more then 5 routes with the same frequency and average filght time.</li>
</ol>

<h2>Schema:</h2>

Input:

<h3><code>flights</code></h3>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>flight_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>origin</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>destination</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>departure_time</td>
<td>DATETIME</td>
</tr>

<tr>
<td>arrival_time</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>origin</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>destination</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>route_frequency</td>
<td>INTEGER</td>
</tr>

<tr>
<td>avg_duration_minutes</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>

<h2>Example:</h2>

<h3>Input Data</h3>

<h3><code>flights</code></h3>

<table>
<thead>
<tr>
<th>flight_id</th>
<th>origin</th>
<th>destination</th>
<th>departure_time</th>
<th>arrival_time</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>JFK</td>
<td>LAX</td>
<td>2025-11-19 08:00</td>
<td>2025-11-19 11:30</td>
</tr>

<tr>
<td>2</td>
<td>JFK</td>
<td>LAX</td>
<td>2025-11-19 14:00</td>
<td>2025-11-19 17:40</td>
</tr>

<tr>
<td>3</td>
<td>JFK</td>
<td>ORD</td>
<td>2025-11-19 09:00</td>
<td>2025-11-19 10:45</td>
</tr>

<tr>
<td>4</td>
<td>JFK</td>
<td>ORD</td>
<td>2025-11-20 09:00</td>
<td>2025-11-20 10:42</td>
</tr>

<tr>
<td>5</td>
<td>JFK</td>
<td>MIA</td>
<td>2025-11-19 12:00</td>
<td>2025-11-19 15:00</td>
</tr>

<tr>
<td>6</td>
<td>JFK</td>
<td>MIA</td>
<td>2025-11-20 12:00</td>
<td>2025-11-20 14:55</td>
</tr>

<tr>
<td>7</td>
<td>LAX</td>
<td>SFO</td>
<td>2025-11-19 07:00</td>
<td>2025-11-19 08:25</td>
</tr>

<tr>
<td>8</td>
<td>LAX</td>
<td>SFO</td>
<td>2025-11-19 16:00</td>
<td>2025-11-19 17:20</td>
</tr>

<tr>
<td>9</td>
<td>LAX</td>
<td>SEA</td>
<td>2025-11-19 06:00</td>
<td>2025-11-19 08:40</td>
</tr>

<tr>
<td>10</td>
<td>LAX</td>
<td>SEA</td>
<td>2025-11-20 06:00</td>
<td>2025-11-20 08:35</td>
</tr>
</tbody>
</table>

<h2>Output</h2>

<table>
<thead>
<tr>
<th>origin</th>
<th>destination</th>
<th>route_frequency</th>
<th>avg_duration_minutes</th>
</tr>
</thead>

<tbody>
<tr>
<td>JFK</td>
<td>LAX</td>
<td>2</td>
<td>210</td>
</tr>

<tr>
<td>JFK</td>
<td>ORD</td>
<td>2</td>
<td>103</td>
</tr>

<tr>
<td>JFK</td>
<td>MIA</td>
<td>2</td>
<td>172</td>
</tr>

<tr>
<td>LAX</td>
<td>SFO</td>
<td>2</td>
<td>82</td>
</tr>

<tr>
<td>LAX</td>
<td>SEA</td>
<td>2</td>
<td>157</td>
</tr>
</tbody>
</table>
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.

You work as a machine learning engineer for a health insurance company. Design a machine learning model, which given a set of health features, classifies if the individual will undergo major health issues or not.

Creating a machine learning model for evaluating a patient's health

Risk Assessment Model

Describe a time when you had to define a long-term vision for a project or team and move it from concept to reality.

Interviews for leadership or senior technical roles look for more than just “finishing a project.” Your answer should specifically address:

<ol>
<li>The “Why”: What data or organizational gap necessitated this vision?</li>
<li>The Framework: How did you translate a high-level goal into a roadmap?</li>
<li>The Friction: How did you handle stakeholders or team members who were skeptical of the new direction?</li>
</ol>

Vision Setting and Execution Strategy

Product Sense & Metrics

Let’s say that you’re a data scientist working for Facebook.

A product manager has asked you to develop a method to match users to their siblings on Facebook.

<ol>
<li>How would you evaluate a method or algorithm to match users with their siblings?</li>

<li>What metrics might you use?</li>
</ol>

Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

Let’s say that you work at Facebook. Your team focuses on helping small businesses increase sales through the Marketplace app on Facebook.

How would you determine the customer service quality through the chat box for all interactions involving small businesses selling items to consumers?

One day you are invited to your kid’s kindergarten. The children there ask about your work as a machine learning engineer.

How would you explain neural networks to the kids there?

Explain Neural Nets to Kids

Let’s say you work at a food delivery company.

How would you measure the effectiveness of giving extra pay to delivery drivers during peak hours to meet the demand from consumers?

Extra Delivery Pay

A/B Testing

Let’s say that you’re working on the Uber app. 

How would you design an incentive scheme for drivers such that they would more likely go into city areas where demand is high?

Tell me about a project in which you had to clean and organize a large dataset.

Let’s say that you’re working on building a model to detect potential bombs at a border crossing.

How would you design the model’s inputs and outputs, measure its accuracy, and test your model?

Let’s say you are building a neutral net to detect if a nuclear missile is incoming.

What metric would be most important to look at?

Let's say you are building a neutral net to detect if a nuclear missile is incoming.

What metric would be most important to look at?

Nuclear Missile Detection

Let’s say you’re given 90 days of ride data.

How would you use the ride data to project the lifetime of a new driver on the system? What about the lifetime value of the driver?

How would you use the ride data to project the lifetime of a new driver on the system?

Lifetime Driver

Let’s you’re tasked with pitching a new feature for Google Home. Your co-worker comes to you with an idea to build a game feature for Google Home.

How would you go about deciding whether Google should build it?

Let’s say you work at Meta (Facebook).

The company is looking to grow its userbase in an emerging market.

How would you approach promoting Meta’s products?

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

Hawaiian airlines Interview Questions

Hawaiian airlines Interview Guides

Hawaiian airlines Interview Questions

Challenge

Discussion & Interview Experiences

Discussion & Interview Experiences