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Herc Rentals Interview Questions

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Machine Learning

How would we build a model to detect fraud and text customers to approve or deny fraudulent transactions

Bank Fraud Model

Rebalancing outcome probabilities for a classifier on imbalanced data.

Rebalance Probabilities

In which case would you use a bagging algorithm versus a boosting algorithm

Bagging vs Boosting

What kind of model did the co-worker develop, how would you measure the difference between the two credit risk models within a timeframe, and what metrics would you track to measure the success of the new model

Loan Model

How to model merchant acquisition in a new market?

Merchant Acquisition

Data Structures & Algorithms

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Write a program in python to determine the TF values for each term of a document

Term Frequency

Cyclic Detection

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

Analytics

How would you present the performance of each subscription to an executive?

Analyzing Churn Behavior

Which metric should we use for late orders if the distribution is right-skewed and what does that tell us?

Late Orders

Let's say you work at Facebook and you're analyzing churn on the platform.

Retention Rate Disparity

Statistics

Explain the difference between covariance and correlation and provide an example

Covariance vs Correlation

What does it mean to "bootstrap" a data set?

Bootstrapping Samples

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

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

Your Strengths and Weaknesses

Founded in 1965, Herc Rentals is a premier equipment rental supplier in North America, boasting nearly $3.3 billion in revenue as of March 31, 2024. Listed on the NYSE under "HRI," Herc Rentals operates through 412 locations with approximately 7,600 employees. Herc Rentals excels in delivering innovative equipment solutions across diverse sectors such as construction, industrial operations, and entertainment production. The company emphasizes technological advancements and exceptional customer and employee experiences through robust IT and digital platforms. Dive into this guide to discover Herc Rentals' dynamic interview process and join a legacy of market-leading expertise and growth. Ready to become part of a leading industry force? Read on!

Founded in 1965, Herc Rentals is a premier equipment rental supplier in North America, boasting nearly $3.3 billion in revenue as of March 31, 2024. Listed on the NYSE under "HRI," Herc Rentals operates through 412 locations with approximately 7,600 employees. Herc Rentals excels in delivering innovative equipment solutions across diverse sectors such as construction, industrial operations, and entertainment production. The company emphasizes technological advancements and exceptional customer and employee experiences through robust IT and digital platforms. Dive into this guide to discover Herc Rentals' dynamic interview process and join a legacy of market-leading expertise and growth. Ready to become part of a leading industry force? Read on!

Overview

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</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>Column</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>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Given two sorted lists, write a function to merge them into one sorted list.

Bonus: What’s the time complexity?

Example:

Input:

<pre tabindex="0" class="chroma"><code>list1 = [1,2,5]
list2 = [2,4,6]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def merge_list(list1,list2) -&gt; [1,2,2,4,5,6]
</code></pre>

You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort?

You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort? 

Merging Lists Complexity

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?

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

What is the difference between covariance and correlation? Provide an example.

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

How does random forest generate the forest and why use it over logistic regression

Random Forest Explanation

Given a <code>ListNode</code> representing the head of a linked list, write a function <code>is_cyclic</code> which returns <code>True</code> if a linked list is cyclic and <code>False</code> if it is not. A linked list is cyclic when there is no tail to the linked list, and the supposed tail is attached to another node inside the list itself, creating a cycle.

A <code>ListNode</code> is defined as:

<pre tabindex="0" class="chroma"><code>class ListNode:
	value: ListNode = None
 next: ListNode = None
</code></pre>

Example:

Input:

<img src="/s3/questions/1a65651d-a483-4481-b073-2738d65ab9d7.png" alt="image"/>

Output:

<pre tabindex="0" class="chroma"><code>def is_cyclic(head) -&gt; True
</code></pre>

Say you are given a text document in the form of a string with the following sentences:

Example:

Input:

<pre tabindex="0" class="chroma"><code>document = &#34;I have a nice car with a nice tires&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>
{
&#34;I&#34;:0.11,
&#34;have&#34;:0.11,
&#34;a&#34;:0.22,
&#34;nice&#34;:0.22,
&#34;car&#34;: 0.11,
&#34;with&#34;:0.11,
&#34;tires&#34;:0.11
}
</code></pre>

Write a program in python to determine the TF (<code>term_frequency</code>) values for each term of this document.

Note: Round the term frequency to 2 decimal points.

Tell me about a data project that didn’t go the way you expected. What did you set out to do, what surprised you, and how did you handle it?

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

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

Let’s say you work for a bank that gives out personal loans. Your co-worker develops a model that takes in customer inputs and returns if a loan should be given or not.

<ol>
<li>What kind of model did the co-worker develop? 
</li>
<li>Another co-worker thinks they have developed a better model to predict defaults on the loans. Given that personal loans are monthly installments of payments, how would you measure the difference between the two credit risk models within a timeframe? 
</li>
<li>What metrics would you track to measure the success of the new model?</li>
</ol>

Imagine you run a pizza franchise, and you run into a problem with a lot of no-shows after customers place their order.

What features would you include in a model to try to predict a no-show?

What features would you include in a model to predict a no-show

Pizza No Show

You work as the sole machine learning engineer at a mid-sized e-commerce company. You have developed a predictive model for customer purchase likelihood that needs to be retrained periodically and reliably served in production. There is no dedicated data engineering or ML-Ops support.

How would you automate the retraining and deployment of this model while ensuring reliability and minimal downtime?

Design an end-to-end retraining and production pipeline for a machine learning model without dedicated infrastructure or support staff.

Solo ML Deployment

ML Ops

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Let’s say we’re launching a new version of the PayPal app, but due to a tight deadline, we don’t have time to run the entire suite of 300 automated test cases before release.

How would you handle this situation?

How would you approach limited testing time before a major app release?

Testing Constraints

A/B Testing

Let’s say you’re trying to calculate churn for a subscription product.

You noticed that of all customers that bought subscriptions in January 2020, about 10% of them canceled their membership before their next cycle on February 1st.  

If you assume that your new customer acquisition is uniform throughout each month and that customer churn goes down by 20% month over month, what’s the expected churn rate in March for all customers that bought the product since January 1st?

What’s the expected churn rate in March for all customers that bought the product since January 1st?

Expected Churn

Product Sense & Metrics

Let’s say you work as a data scientist at DoorDash. 

You are tasked to build a machine learning system that minimizes missing or wrong orders placed on the app.

How would you go about designing this system?

Minimize Wrong Orders

Imagine you are trying to track the position of a moving car using noisy GPS data, how would you explain the Kalman filter to someone with no technical background?

Use a real-world analogy to make your explanation accessible.

Explain the Kalman filter in simple, real-world terms.

Kalman Filter in GPS tracking

Forecasting & Time Series

Let’s say that you work for a company like Netflix. Netflix has two pricing plans: $15/month or $100/year.

Let’s say an executive at the company wants you to analyze the churn behavior of users that subscribe to either plan.

What kinds of metrics / graphs / models would you build to help give the executive an over-arching view of how the subscriptions are performing?

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?

Suppose we are training a binary classification algorithm. 

The outcome is imbalanced, with 99.8% of individuals in our sample having an outcome value of 0, and 0.2% having an outcome value of 1. To build a model, we down-sample the training data using a random sample of 1% of the individuals who have an outcome of 0 and keeping all individuals with an outcome of 1.

Now we train the model on the smaller sample and build a binary classifier that predicts the probability of an individual with a value of 1.

How would we then adjust our output probabilities to use this model on the total, imbalanced population?

Suppose you are given three models for a binary classification problem. Each model gives you a probability of an input belonging to class 1.

You are asked to build an area under the ROC curve metric to evaluate the model performance. Briefly explain how you will do that.

After you make the metric and make sure it works correctly, you apply it on three models and get the following results:

<ul>
<li>Model 1: 0.1</li>
<li>Model 2: 0.5</li>
<li>Model 3: 0.8</li>
</ul>

Briefly explain your evaluation of these models and choose one to use for the classifier.

Explain how to build an AUC metric and evaluate three binary classification models using it

Area Under the ROC Curve

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 work at a food delivery company. You’re tasked with discovering how late the late orders actually are.

<ol>
<li>Which is a better metric to look at? The average or the median?</li>
<li>Let’s say that you model out the distribution and it’s right-skewed. Which metric should we use then?</li>
<li>What does that tell us about the late orders?</li>
</ol>

Let's say that you work at a food delivery company. You're tasked with discovering how late the late orders actually are.

What's best way you can investigate this?

Let's say that you work at a food delivery company. You're tasked with discovering how late the late orders actually are.

What's best way you can investigate this?

Let’s say you work at Facebook and you’re analyzing churn on the platform.

<ol>
<li>How would you investigate if a disparity exists in retention on different Facebook products?</li>

<li>How would you investigate the causes of such a disparity?</li>
</ol>

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.

Explain what backpropagation does, its informal intuition, drawbacks, and derive/prove it

Backpropagation Explanation

What does it mean to “bootstrap” a data set?

Give an example of a time when it would be appropriate to bootstrap a smaller sample(s) from a larger data set.

What are the advantages and disadvantages of doing so?

How would you explain the bias variance tradeoff in machine learning to a high school student?

Replaced by QUESTION 791 - Should be deleted

Bias Variance Tradeoff

As a data scientist at a mortgage bank, how would you approach building a predictive model for loan default risk?

As a data scientist at a mortgage bank, how would you approach building a predictive model for loan default risk?

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

Herc Rentals Interview Questions

Herc Rentals Interview Guides

Overview

Herc Rentals Interview Questions

Challenge

Discussion & Interview Experiences

Discussion & Interview Experiences