Prepare for and practice interview questions from United Airlines across topics like Takehome, SQL, Machine Learning and more.

United Airlines Interview Questions

United Airlines Interview Guides

Machine Learning

Encoding Categorical Features

Bank Fraud Model

Type-ahead Search

How to model merchant acquisition in a new market?

Merchant Acquisition

Food Prep Features

Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

Employee Salaries

User Experience Percentage

Distance Traveled

Download Facts

Select a (weight) random driver from the database.

Random Weighted Driver

Data Structures & Algorithms

Given a list of integers, and an integer N, write a function to find all combinations that sum to the value N.

Sum to N

Write code to generate a sample from a multinomial distribution with keys 

Drawing Balls From Bin

Weighted Keys

Simulate rolling a dice from a continuous uniform distribution

Dice Rolls From Continuous Uniform

Justify an array of words given an arbitrary line width.

Max Width

Statistics

P-value to a Layman

What are the assumptions of linear regression?

Assumptions of Linear Regression

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?

Boarding Times Bias

MLE vs MAP

Describe linear regression to various audiences with different levels of knowledge.

Explaining Linear Regression to Different Audiences

Business Case

Instant Eat Reintroduction

Delivery Fees

Apartment Pricing

How to boost presence in high-demand city areas

Incentive Scheme

How would you create a policy for refunds with regards to balancing customer sentiment and goodwill versus revenue tradeoffs?

Food Delivery Refund Policy

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

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

Your Strengths and Weaknesses

Brainteasers

How would you explain what a p-value is to someone who is not technical?

What does a p-value in a statistical test represent?

What does a p-value in a statistical test represent?

P-value to a Layman I

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

P-value to a Layman II

What are the assumptions of linear regression?

Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

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

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Analytics

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

Xgboost vs Random Forest

How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?

What happens when you average the output on multiple decision trees?

What happens when you average the output on multiple decision trees?

Average Trees

Random Forest Explanation

Let’s say that you work at a B2B SAAS company that’s interested in testing the pricing of different levels of subscriptions.

Your project manager comes to you and asks you to run a two-week-long A/B test to test an increase in pricing.

How would you approach designing this test? How would you determine whether the increase in pricing is a good business decision?

Testing Price Increase

A/B Testing

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Experiment Validity

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

How would we build this model?

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

What’s the relationship between PCA and K-means clustering?

What does the variable “k” in k-means clustering refer to?

What does the variable "k" in k-means clustering refer to?

Input of K-means

PCA and K-Means

How would you tackle multicollinearity in multiple linear regression?

Multicollinearity in Regression

In the context of hypothesis testing, what are type I errors (type one errors) and type II errors (type two errors)? What is the difference between the two?

Bonus: Describe the probability of making each type of error mathematically.

What is the difference between type I and type II errors?

Type I and II Errors

What does the backpropagation algorithm do in the context of neural networks? What is the informal intuition behind the algorithm? What are some drawbacks of the algorithm compared to other optimization methods?

Bonus: Formally derive the backpropagation algorithm and prove that it does what it claims to do.

Backpropagation Explanation

Given a <code>employees</code> and <code>departments</code> table, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

Example:

Input:

<code>employees</code> table

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td><code>percentage_over_100k</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>number_of_employees</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

How would you explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician? Ensure your explanations are tailored to the understanding level of each audience.

Implement the k-means clustering algorithm in python from scratch, given the following:

<ul>
<li>A two-dimensional NumPy array <code>data_points</code> that is an arbitrary number of data points (rows) <code>n</code> and an arbitrary number of columns <code>m</code>.</li>
<li>Number of k clusters <code>k</code>.</li>
<li>The initial centroids value of the data points at each cluster <code>initial_centroids</code>.</li>
</ul>

Return a list of the cluster of each point in the original list data_points with the same order (as a integer).

Example

<img src="https://i.ibb.co/KKp2gPn/kemans-before.png" alt="before clustering"/>

After clustering the points with two clusters, the points will be clustered as follows.

<img src="https://i.ibb.co/bdwVWxR/kemans-example-after.png" alt="after clustering"/>

Note: There could be an infinite number of separating lines in this example.

Example

<pre tabindex="0" class="chroma"><code>
#Input
data_points = [(0,0),(3,4),(4,4),(1,0),(0,1),(4,3)]
k = 2
initial_centroids = [(1,1),(4,5)]


#Output 

k_means_clustering(data_points,k,initial_centroids) -&gt; [0,1,1,0,0,1]

</code></pre>

<img src="https://i.ibb.co/pRsRz12/example.png" alt=""/>

Implement the k-means clustering algorithm in python from scratch

k-Means from Scratch

Let’s say you have a categorical variable with thousands of distinct values, how would you encode it?

Which method can be used to extract communities from large networks, that does not require a pre-determined number of clusters like K-means?

Which method can be used to extract communities from large networks, that does not require a pre-determined number of clusters like K-means?

Let’s say that you’re a data scientist at DoorDash.

How would you build a model to predict which merchants the company should go after for acquisition when entering a new market?

We want to determine if Uber Eats has a net positive value for the company Uber.

How would you measure the success of Uber Eats?

If Uber wants to evaluate the customer loyalty and repeat business on Uber Eats, which of the following metrics would be the most relevant?

If Uber wants to evaluate the customer loyalty and repeat business on Uber Eats, which of the following metrics would be the most relevant?

Uber Eats Success I

Which among the following metrics would be most indicative of the operational efficiency of Uber Eats?

Which among the following metrics would be most indicative of the operational efficiency of Uber Eats?

Uber Eats Success II

Which among the following metrics would be most critical in evaluating the overall profitability of Uber Eats for Uber?

Which among the following metrics would be most critical in evaluating the overall profitability of Uber Eats for Uber?

Uber Eats Success III

Uber Eats Success

Product Sense & Metrics

What are the benefits of dynamic pricing, and how can you estimate supply and demand in this context?

Achieving maximum profits through dynamic pricing with real-time supply and demand fluctuations

Understanding Dynamic Pricing Strategy

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

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>

Build a k Nearest Neighbors classification model from scratch.

KNN From Scratch

To improve customer experience on Uber Eats, what key parameters would you focus on improving?

Delivering an exceptional customer experience by focusing on key customer-centric parameters

Uber Eats Customer Experience

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

Write a function to get a sample from a standard normal distribution.

Write a function to get a sample from a standard normal distribution.

Normal Distribution Sample

Probability

How would you build the recommendation algorithm for type-ahead search for Netflix?

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?

You work as a data scientist on a ride sharing marketplace.

<ol>
<li>What metrics would you look at to determine the demand for rides at any point?</li>

<li>What metrics would tell you if there is high demand and low supply? How can you determine the threshold for when there’s too much demand?</li>
</ol>

As a data scientist in a ride sharing marketplace, which metrics could help determine the demand for rides at any point?

As a data scientist in a ride sharing marketplace, which metrics could help determine the demand for rides at any point?

Demand Metrics I

In the context of a ride sharing marketplace, which situation would indicate a high demand and low supply?

In the context of a ride sharing marketplace, which situation would indicate a high demand and low supply?

Demand Metrics II

How would you identify supply and demand mismatch in a ride sharing market place?

Demand Metrics

Let’s say we want to build a model to predict the time spent for a restaurant to prepare food from the moment an order comes in until the order is ready. 

What kind of model would we build and what features would we use?

Let’s say you’re working on DoorDash demand side deliveries. DoorDash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers (delivery drivers).

Let’s say we want to create a model to determine which Dashers should deliver a particular order.

We currently have these features already in our model.

<ul>
<li>Dasher distance to the restaurant</li>
<li>Number of Dasher reviews from customers</li>
<li>Number of Dasher successful deliveries</li>
</ul>

Which feature would be the best additional input to our existing features to improve the model?

Let's say you're working on DoorDash demand side deliveries. DoorDash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers (delivery drivers).

Let's say we want to create a model to determine which Dashers should deliver a particular order.

We currently have these features already in our model. 

- Dasher distance to the restaurant
- Number of Dasher reviews from customers
- Number of Dasher successful deliveries

Which feature would be the best additional input to our existing features to improve the model?



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

United Airlines Interview Questions

United Airlines Interview Guides

United Airlines Interview Questions

Challenge

United Airlines Salaries by Position

Discussion & Interview Experiences

Discussion & Interview Experiences