Prepare for and practice interview questions from Thrive Market.

Thrive Market Interview Questions

Thrive Market Interview Guides

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

Comments Histogram

Write a query that returns all neighborhoods that have 0 users.

Empty Neighborhoods

Closest SAT Scores

Subscription Overlap

Data Structures & Algorithms

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

Merge Sorted Lists

Compute Deviation

Get Top N Frequent Words

String Shift

Weekly Aggregation

Machine Learning

Encoding Categorical Features

Coefficients of Logistic Regression

Booking Regression

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

Rejection Reason

Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

Perfectly Separable

A/B Testing

Experiment Validity

How do we go about selecting the best 10,000 customers for the pre-launch?

Pre-Launching Shows

Button AB Test

A/B Test Power Size

Network Experiment Design

Statistics

P-value to a Layman

Reducing Error Margin

What are the assumptions of linear regression?

Assumptions of Linear Regression

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

Hundreds of Hypotheses

Using R Squared

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

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

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

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

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

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Data Pipelines

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 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

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?

Bank Fraud Model

Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

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?

Lasso vs Ridge

<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

How would you convey insights and the methods you use to a non-technical audience?

Making data-driven insights actionable for those without technical expertise

Simple Explanations

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

Let’s say you’re a data engineer at Fidelity Investments, and you’re running a SQL query on a cloud-based data warehouse. All cluster resources and network health metrics look normal, but the query is still taking over 10 minutes to complete.

How would you go about diagnosing and improving the performance of this query?

How would you diagnose and speed up a slow SQL query when system metrics look healthy?

Slow SQL Query

Query Optimization

Challenge

<h5>Product Manager, Member Growth </h5>Thrive Market is seeking a Product Manager to drive key initiatives across member acquisition, onboarding, and early engagement. You will own a defined set of growth surfaces within our e-commerce and membership experience, partnering closely with Engineering, Design, Data, and Growth Marketing to run experiments, ship improvements, and drive measurable business impact. This role is ideal for a Product Manager who thrives in fast-paced environments, is deeply comfortable with data, and enjoys turning insights into shipped products and learning loops. You will play a critical role in improving how new members discover, experience, and realize value from Thrive Market. <h5>Responsibilities </h5><ul> <li>Own product discovery and delivery for a defined area of the member growth funnel, such as acquisition, onboarding, or early retention </li><li>Partner with Growth Marketing, Engineering, Design, and Data Science to identify opportunities and prioritize experiments that improve conversion, engagement, and retention </li><li>Define hypotheses, success metrics, and experiment plans. Run A/B tests and analyze results to inform iteration and roadmap decisions </li><li>Translate user research, behavioral data, and business goals into clear product requirements and execution plans </li><li>Collaborate on improvements to subscription flows, value messaging, and member journeys, with a focus on delivering customer and business value </li><li>Communicate progress, learnings, and outcomes clearly to stakeholders and leadership </li><li>Contribute to a strong product culture by participating in planning, retros, and continuous improvement rituals </li></ul> <h5>Qualifications </h5><ul> <li>3+ years of product management experience, preferably in growth, retention, or lifecycle-focused roles </li><li>Experience working on subscription, membership, or e-commerce products </li><li>Demonstrated success improving acquisition, onboarding, engagement, or retention metrics </li><li>Strong analytical skills. Comfortable working with data, defining metrics, and using insights to drive decisions </li><li>Experience shipping experiments and iterating based on results </li><li>Strong cross-functional collaboration skills and ability to influence without authority </li><li>Clear communicator who can align stakeholders around goals, tradeoffs, and outcomes </li><li>Passion for Thrive Market's mission and empathy for our members </li></ul> At Thrive Market, we believe in building a diverse, inclusive, and authentic culture. If you are excited about this role along with our mission and values, we encourage you to apply.

<h5>Overview </h5>About Thrive Market: Thrive Market was founded in 2014 with a mission to make healthy and sustainable living easy and affordable for everyone. As an online, membership-based market, we deliver high-quality healthy and sustainable products at member-only prices, while matching every paid membership with a free one for someone in need. We are a Certified B Corporation, a Public Benefit Corporation, and a Climate Neutral Certified company. Join us as we bring healthy and sustainable living to millions of Americans in the years to come. <h5>The Role </h5>At Thrive Market, data is core to how we operate, innovate, and grow. We are looking for a Senior Data Analyst, Strategy to play a critical role in turning data into strategic insight across the business. This isnt a role where youll be siloed in one function. Youll be a strategic partner to leadership across Merchandising, Member Experience, Marketing, and beyond. Youll own ambiguous problems, build the frameworks to solve them, and influence how leaders across Thrive Market think about growth, profitability, and member satisfaction. This role goes beyond building dashboards or pulling reports. Reporting into the Data Engineering Manager, youll help translate complex data into clear, actionable insights that shape business outcomes. Whether its analyzing pricing strategies, identifying retention opportunities, evaluating product performance, or supporting cross-functional initiatives, youll bring rigor, curiosity, and clarity to every conversation. Youll be a trusted analytics partner to executives and functional leaders across Thrive Market, connecting data to real-world outcomes, and helping teams make smarter, faster decisions every day. This role will initially be embedded with a specific function based on business priorities, but youll have opportunities to flex across teams as strategic needs evolve. <h5>Responsibilities </h5><ul> <li>Strategic Partnership & Business Impact <ul><li>Lead high-impact, end-to-end analytics projects that inform key business and product strategies across multiple functions. </li><li>Partner cross-functionally with stakeholders to align on priorities and translate ambiguous business questions into analytical frameworks. </li><li>Deliver clear, data-driven insights and recommendations that influence decision-making at the executive level. </li><li>Own the "so what" not just the "what." Youll define the right questions to ask, not just answer the ones given to you. </li><li>Bring thought leadership to Thrive Markets data practices, recommending new ways to enhance our analytical toolkit and processes. </li></ul> </li><li>Analytics Execution & Technical Depth <ul><li>Design and maintain dashboards, models, and datasets that empower business teams to self-serve and track performance. </li><li>Leverage external and internal data to uncover opportunities in pricing, assortment, member engagement, and retention. </li><li>Execute deep-dive analysis to ensure business strategies are competitive, high-performance, and profitable. </li><li>Identify data quality issues and help resolve inconsistencies across source systems. </li><li>Respond to ad hoc analytical requests with accuracy, efficiency, and business context. </li></ul> </li><li>Cross-Functional Collaboration <ul><li>Collaborate with Finance to measure and forecast the financial impact of product and business decisions. </li><li>Support lifecycle and engagement analytics to inform retention, LTV, and user journey improvements. </li><li>Partner with Merchandising, Member Experience, Marketing, and Operations teams to surface actionable insights that drive outcomes. </li><li>Build and maintain reporting frameworks that track and visualize key insights across category-level KPIs, member engagement metrics, and financial performance. </li></ul> </li><li>Domain Flexibility (Based on Business Need) <ul><li>You may support Merchandising by analyzing product performance, assortment effectiveness, and pricing/promotional impact. </li><li>You may support Member Experience by analyzing the end-to-end customer journey, identifying friction points, and surfacing strategies to improve retention and reduce support volume. </li><li>You may support Marketing by evaluating campaign performance, channel effectiveness, and acquisition/retention trade-offs. </li><li>Regardless of where you start, youll have the opportunity to expand your scope and become a go-to strategic resource across the business. </li></ul> </li></ul> <h5>Qualifications </h5>Required: <ul> <li>MBA with a focus in analytics, strategy, or a related quantitative field (or 7+ years of exceptional experience demonstrating equivalent strategic impact). </li><li>5-7+ years of experience in data analytics, with a strong track record of influencing business decisions and driving measurable outcomes. </li><li>Advanced SQL skills (CTEs, window functions, complex joins, subqueries) and proficiency in Python. </li><li>Experience with large-scale data tools such as Hive, Spark, or similar. </li><li>Hands-on experience with Snowflake or other modern cloud data warehouses. </li><li>Strong foundation in statistics and multivariate regression. </li><li>Expertise in data visualization and BI tools (Domo, Tableau, Looker, etc.). </li><li>Ability to clearly communicate insights to non-technical audiences and influence decision-making at senior levels. </li><li>Curious, collaborative, and highly self-motivated with a bias for action and a business-first mindset. </li><li>Demonstrated ability to work across multiple business functions and learn new domains quickly. </li></ul> Nice To Have <ul> <li>Experience in merchandising analytics, pricing strategy, promotional analysis, or category management. </li><li>Background in member experience, retention analytics, lifecycle marketing, or customer journey optimization. </li><li>Experience in eCommerce, subscription businesses, or CPG. </li><li>Strategy consulting background (McKinsey, BCG, Bain, or similar firms). </li><li>Familiarity with A/B testing, web analytics (GA, Amplitude, Mixpanel), and experimentation frameworks. </li><li>Experience presenting to and influencing C-suite or VP-level stakeholders. </li><li>Track record of operating in lean, fast-moving environments where youve had to build structure from ambiguity. </li></ul> <h5>Belong To a Better Company </h5><ul> <li>Comprehensive health benefits (medical, dental, vision, life and disability) </li><li>Competitive salary (DOE) + equity </li><li>401k plan </li><li>9 Observed Holidays </li><li>Flexible Paid Time Off </li><li>Subsidized ClassPass Membership with access to fitness classes and wellness and beauty experiences </li><li>Ability to work in our beautiful office in Playa Vista </li><li>Free Thrive Market membership with exclusive employee discount </li><li>Coverage for Life Coaching & Therapy Sessions on our holistic mental health and well-being platform </li></ul> Were a community of more than 1 million members who are united by a belief: It should be easy to find better products, support better brands, make better choices, and build a better world in the process. Thrive Market is an inclusive employer. If you need assistance or accommodation due to a disability, please email eeo@thrivemarket.com. Thrive Market operates a legitimate job process and never requests payment from applicants. For security, apply through our company website at www.thrivemarket.com. Thrive Market 2026 All rights reserved. <h5>Job Information </h5><ul> <li>Compensation Description - The base salary range for this position is $130,000 - $170,000/Per Year. </li><li>Compensation may vary outside this range depending on qualifications, skills, competencies and experience, and geographic location. </li><li>Total Compensation includes Base Salary, Stock Options, Health & Wellness Benefits, Flexible PTO, and more! </li><li>This position requires traveling to our HQ office in Los Angeles, California, twice a year for all-company summits; once in the summer and once in the winter. </li></ul> #J-18808-Ljbffr

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

Thrive Market Interview Questions

Thrive Market Interview Guides

Thrive Market Interview Questions

Challenge

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Discussion & Interview Experiences

Discussion & Interview Experiences