Prepare for and practice interview questions from Everquote.

Everquote Interview Questions

Everquote Interview Guides

Data Structures & Algorithms

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

Merge Sorted Lists

Find Bigrams

Get Top N Frequent Words

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

Given two nonempty lists of user_ids and tips, write a function to find the user that tipped the most.

Biggest Tip

Machine Learning

Encoding Categorical Features

How do we give each rejected applicant a reason why they got rejected?

Rejection Reason

Job Recommendation

Bank Fraud Model

Lasso vs Ridge

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

Select the 2nd highest salary in the engineering department

2nd Highest Salary

User Experience Percentage

Distance Traveled

Write a query to retrieve the number of users that have posted each job only once and the number of users that have posted at least one job multiple times.

Repeat Job Postings

Probability

First to Six

500 Cards

Raining in Seattle

Found Item

Given three uniform(0,4) random variables, what is the probability that the median of them is greater than 3?

Median Probability

A/B Testing

Experiment Validity

Delivery Estimate Model

Button AB Test

A/B Test Power Size

Green Dot

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

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

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

Describing a data project and its challenges

Hurdles In Data Projects

Analytics

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

P-value to a Layman

Statistics

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

What are the assumptions of linear regression?

Assumptions of Linear Regression

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

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 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

Bagging vs Boosting

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

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>

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

String Palindromes

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>

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

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

What is the difference between Logistic and Linear Regression?

When would use one instead of the other in practice?

What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

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

Create a class named <code>LRUCache</code> that implements the behavior expected of a Least Recently Used (LRU) Cache.

The <code>LRUCache</code> class implements the following methods:

<ul>
<li><code>__init__(self, capacity)</code> - initializes the object and takes in a capacity parameter specifying the maximum capacity of the cache.</li>
<li><code>get_if_exists(self, key) -&gt; Any|None</code> - gets a value based on a key. If the key exists, returns the associated value. If the key has expired or did not exist in the first place, returns <code>None</code>.</li>
<li><code>put(self, key, value)</code> - places a value inside the cache. In the case wherein the cache is full, invalidates the least recently used element. When two keys collide, the older value should be invalidated in place of the new one.</li>
</ul>

Example:

<pre tabindex="0" class="chroma"><code>cache = LRUCache(2)
cache.put(&#34;sample&#34;, 55)
cache.get_if_exists(&#34;sample&#34;) # returns 55
cache.get_if_exists(&#34;key&#34;) # returns None
cache.put(&#34;hello&#34;, 10) 
cache.put(&#34;sample&#34;, 9)
cache.put(&#34;world&#34;, 5)
cache.get_if_exists(&#34;hello&#34;) # returns None
</code></pre>

Notes:

<ul>
<li>It is assured that the value in the <code>put</code> method will never receive a <code>None</code> value. Moreover, it is assured that the capacity will always be &gt; 0.</li>

<li>All operations must be O(1).</li>

<li>Aside from Python’s standard dictionary implementation (<code>{}</code>), you may not use any other built-in data structures.</li>
</ul>

LRU Cache 1

A team wants to A/B test multiple different changes through a sign-up funnel.

For example, on a page, a button is currently red and at the top of the page. They want to see if changing a button from red to blue and/or from the top of the page to the bottom of the page will increase click-through.

How would you set up this test?

Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

A/B Test Variants

Let’s say we want to launch a re-design of a landing page to improve the click-through rate. We can do this by implementing an AB test.
Given that we launch an AB test, how would you infer if the results of the click-through rate were statistically significant or not?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

Statistically Significant Test

Precisely ascertain whether the outcomes of an A/B test, executed to assess the impact of a landing page redesign, exhibit statistical significance.

Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.

Note: Can you do it on $O(n)$ time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</code></pre>

Given an array and a target integer, write a function that returns the indices of two integers in the array that add up to the target integer.

Target Indices

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?

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 you work at Allstate. Allstate is running <code>N</code> online ads right now. The table <code>ads</code> contains all those ads, ranked by popularity via the <code>id</code> column (e.g., the entry with <code>id = 1</code> is the most popular, etc.).

Create a subquery or common table expression named <code>top_ads</code> containing the top 3 ads (by popularity) and return the number of rows that would result from the following operations

<ol>
<li><code>ads INNER JOIN top_ads</code></li>
<li><code>ads LEFT JOIN top_ads</code></li>
<li><code>ads RIGHT JOIN top_ads</code></li>
<li><code>ads CROSS JOIN top_ads</code></li>
</ol>

Note: Please make the <code>join_type</code> column in your output have the values <code>inner_join</code>, <code>left_join</code>, etc. for each of their respective join types

Note: Please return only one query with each number in a different row

Example:

Input:

<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>join_type</code></td>
<td>VARCHAR</td>
</tr>

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

Size of Joins

Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.

When should you use one versus the other?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

Regularization and Validation

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

<h1>Binary Tree Validation</h1>

You are given the root of a binary tree. You need to determine if it is a valid binary search tree (BST).

A valid BST is defined as follows:

<ul>
<li>The left subtree of a node contains only nodes with values less than or equal to the node’s value.</li>
<li>The right subtree of a node contains only nodes with values greater than or equal to the node’s value.</li>
<li>Both the left and right subtrees must also be binary search trees.</li>
</ul>

Given the function <code>def is_valid_bst(root: Node) -&gt; bool:</code>, return True if the binary tree is a valid BST. Otherwise, return False.

<h3>Example:</h3>

Input:

<img src="https://d2qpirhrfplx04.cloudfront.net/9664817c-3e12-4a40-9236-8382b13f55a0.png" alt="image"/>Converted Binary Tree.png

Output:

<pre tabindex="0" class="chroma"><code>def is_valid_bst(Node(3)) -&gt; True
</code></pre>

Given the root node, verify if a binary search tree is valid or not.

Binary Tree Validation

Let’s say you are working on Google Docs. A product manager comes to you and asks how the product is doing. 

What are the top five metrics that you would start tracking to understand the health of Google Docs?

Docs Metrics

Product Sense & Metrics

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

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>n = 5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

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

Challenge

Everquote Salaries by Position

<h3>Job Description</h3><div>Job Description<div>This role is ideal for someone who loves working with data, solving complex problems, and making a measurable impact. Analysts are not just a reporting function at EverQuote; they lead cross functional projects and data-driven decision-making, the core of our operational philosophy as we revolutionize how people shop for insurance.We are looking for someone with analytical ability, an interest in understanding complex business and marketplace dynamics, and commitment to operational excellence and continuous improvement.This is a hybrid role that requires being in office several days a week.The base salary range for this full-time hybrid position is $65,000 - $90,000. Starting salaries will generally be in the lower part of the salary range provided. Employees, depending on their specific role, level and responsibilities, may be eligible to participate in company variable compensation or equity programs.What you'll do:<ul><li>Analyze performance metrics and trends in order to identify opportunities to grow our business</li><li>Drive performance marketing or bidding through rapid testing and building scalable capabilities</li><li>Collaborate with product, engineering, and business development teams on cross-functional initiatives</li><li>Interface with company leadership on your insights and recommendations</li><li>Leverage your technical skills to automate operations, reporting, and modeling</li></ul>Who you are:<ul><li>Strong analytical mindset with a degree in Statistics, Mathematics, Economics, Computer Science, related field, OR demonstrated analytical and technical experience</li><li>Able to structure and break down a complex problem into its component parts</li><li>Entrepreneurial spirit with a strong sense of urgency and accountability</li><li>Proficient in data analysis and with an understanding of statistics</li><li>Capable of using data to tell a story and drive change</li><li>You might know SQL or a programming language such as Python (a plus!)</li><li>You are a team player and have the ability and desire to collaborate effectively</li><li>Excited to relocate to Boston, or already living here!</li></ul>About EverQuoteEverQuote operates a leading online marketplace for insurance shopping, connecting consumers with insurance provider customers, which includes both carriers and agents. Our vision is to be the leading growth partner for property and casualty, or P&C, insurance providers. Our results-driven marketplace, powered by our proprietary data and technology platform, is improving the way insurance providers attract and connect with consumers shopping for insurance.For more information, visit https://investors.everquote.com and follow on LinkedIn.</div></div>

<h3>Job Description</h3><div>Job Description<div>EverQuote is in search of a Product Manager for the Site Experiences team; you'll play a pivotal role in shaping and optimizing the consumer-facing experiences and underlying infrastructure of EverQuote's marketplace platform. Your mission will be to deliver seamless, scalable, and high-performing site functionality that drives engagement and value for both shoppers and insurance providers.This role will initially focus on EverQuote's new upper funnel platform, helping build a brand-forward, AI-optimized content and quoting experience. You'll work on a blend of front-end experiences, modular infrastructure, SSR optimization, SEO, and quote APIs, ensuring our site is discoverable, differentiated, and conversion-ready in the new world of AI-driven discovery.This is a hybrid role requiring being in office several days a week. The base salary range for this full-time hybrid position is $125,000-152,000. Starting salaries will generally be in the lower part of the salary range provided. Employees, depending on their specific role, level and responsibilities, may be eligible to participate in company variable compensation or equity programs.What You'll Do:As a Product Manager on the Site Experiences team, you'll work on a wide range of initiatives spanning technical infrastructure, site experiences, content platforms, and operational excellence.<ul><li>Partner with design and engineering to launch intuitive, high-converting site experiences that delight shoppers and build trust</li><li>Support rollout of new quoting infrastructure optimized for AI and LLM discovery, real-time rates, and modular content experiences</li><li>Collaborate with engineering teams to prioritize and execute technical upgrades, ensuring high site performance and maintainability</li><li>Work on initiatives that enhance performance (e.g., Core Web Vitals, SSR, etc.)</li><li>Define the roadmap for your areas of ownership, aligning with EverQuote's broader goals and mission</li><li>Act as the central point of contact for your projects, working across product, engineering, design, analytics, and marketing</li><li>Use data and analytics to measure impact, iterate based on insights, and drive continuous improvement</li><li>Shape the early roadmap for upper funnel SEO/AI discovery channels, including quote surfacing, Schema markup, and LLM-readiness</li></ul>Who you are:<ul><li>5+ years of experience as a Product Manager, ideally with exposure to both technical and UI-driven projects</li><li>Bachelor's degree in a relevant field</li><li>A strong technical foundation, you can work closely with engineers, understand technical constraints, and contribute to discussions on architecture and scalability</li><li>Proven ability to manage projects end-to-end, from discovery and definition to delivery and measurement</li><li>Excellent communication skills, you can distill complex ideas into clear, actionable steps for diverse audiences</li><li>Experience collaborating with cross-functional teams, including design, engineering, and analytics, to deliver impactful outcomes</li><li>A customer-focused approach, you prioritize solving user problems and delivering business value</li><li>Familiarity with Agile methodologies and tools like Jira, Confluence, or similar</li><li>(Bonus): Familiarity with SEO, AEO, CMS tools (e.g. Contentful, Sanity), or AI-driven discovery models (LLMs, ChatGPT traffic, etc.) is a plus</li><li>Bachelor's degree or equivalent experience in a relevant field</li></ul>About EverQuoteEverQuote operates a leading online marketplace for insurance shopping, connecting consumers with insurance provider customers, which includes both carriers and agents. Our vision is to be the leading growth partner for property and casualty, or P&C, insurance providers. Our results-driven marketplace, powered by our proprietary data and technology platform, is improving the way insurance providers attract and connect with consumers shopping for insurance.For more information, visit https://investors.everquote.com and follow on LinkedIn.</div></div>

<h3>Job Description</h3><div>Job Description<div>EverQuote is seeking a hands-on Email & SMS Marketing Analyst to help build our upper-funnel lifecycle program from the ground up. This is a highly collaborative role—partnering with Product, Design, and Engineering—perfect for someone who is technically curious, obsessed with A/B testing, and ready to deepen their analytical skills by building a program from scratch. This role is a unique opportunity for an early-career marketer to move beyond "operating a machine" to actually designing one. You will translate high-level strategy into high-impact campaigns, using structured testing and data insights to shape how we engage millions of consumers at the start of their insurance journey.This is a hybrid role that requires being in office several days a week.The base salary range for this full-time hybrid position is $80,000-119,000. Starting salaries will generally be in the lower part of the salary range provided. Employees, depending on their specific role, level and responsibilities, may be eligible to participate in company variable compensation or equity programs.What you'll do:<ul><li>Own the planning and execution of email and SMS campaigns for Upper Funnel and Marketing Services audiences (e.g., education, brand nurture, re-engagement, lead warm-up).</li><li>Build, schedule, and QA campaigns end-to-end in the email/SMS platform and ensure they adhere to brand guidelines, compliance requirements, and deliverability best practices.</li><li>Drive the definition, maintenance, and evolution of audience segments for Upper Funnel and Marketing Services email and SMS programs.</li><li>Partner with stakeholders to improve targeting precision, personalization, and segment logic based on user behavior and data availability.</li><li>Own the development and execution of email and SMS creative for Upper Funnel campaigns, including subject lines, body copy, and message variants.</li><li>Design, launch, and evaluate creative tests (e.g., subject lines, send times, message framing, CTAs, layouts), using results to inform future campaigns.</li><li>Build and maintain a library of high-performing templates, messages, and creative patterns by audience and funnel stage.</li><li>Analyze campaign performance data using marketing platforms and internal reporting tools.</li><li>Optimize campaigns, audience strategies, cadence, and testing based on performance insights.</li><li>Build the Upper Funnel and Marketing Services email/SMS programs by owning pilots, experiments, and new flows. Work closely with: </li><ul><li>Upper Funnel Marketing Team and Program Owner / VP of Performance Marketing for designing and aligning campaigns to the broader Upper Funnel strategy. </li><li>Senior Manager, Remarketing Analytics for planning, measurement, and prioritization.</li><li>Engineering and Platform teams to help evaluate and implement the right tools and integrations for email/SMS execution and data flows.</li><li>Design, Product, and Analytics partners to ensure messaging, timing, and targeting align with the broader consumer experience.</li></ul></ul>Who you are:<ul><li>Bachelor's degree in Marketing, Business, Economics, Statistics, or a related quantitative field.</li><li>1-4 years of experience in email, SMS, CRM, lifecycle marketing, or a closely related digital marketing role (agency or in-house).</li><li>Hands-on experience using at least one modern email marketing platform (e.g., Iterable, Klaviyo, Mailchimp, Salesforce Marketing Cloud, or similar).</li><li>Familiarity with core lifecycle/email concepts:</li><ul><li>List management and segmentation</li><li>Consent/opt-out handling</li><li>Basic deliverability concepts (bounces, complaints, sender reputation)</li><li>A/B testing and campaign experimentation</li></ul><li>Comfortable working with data in spreadsheets and/or reporting tools to answer basic performance questions. </li><li>Impeccable attention to detail and process discipline for building, QAing, and launching campaigns.</li><li>Strong communication skills</li><li>Ability to prioritize and manage multiple campaigns and tasks in a fast-paced, test-and-learn environment.</li></ul>Nice to have (but not required):<ul><li>Basic familiarity with SQL or BI tools for more advanced analysis (can be learned on the job).</li></ul>About EverQuoteEverQuote operates a leading online marketplace for insurance shopping, connecting consumers with insurance provider customers, which includes both carriers and agents. Our vision is to be the leading growth partner for property and casualty, or P&C, insurance providers. Our results-driven marketplace, powered by our proprietary data and technology platform, is improving the way insurance providers attract and connect with consumers shopping for insurance.For more information, visit https://investors.everquote.com and follow on LinkedIn.</div></div>

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

Everquote Interview Questions

Everquote Interview Guides