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Attentive Interview Questions + Guide in 2025

Attentive Interview Guides

Data Structures & Algorithms

Compute Deviation

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

Merge Sorted Lists

Find Bigrams

Write a function to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Complete Addresses

Get Top N Frequent Words

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

Calculate the first touch attribution for each `user_id` that converted.

First Touch Attribution

Closest SAT Scores

Machine Learning

Encoding Categorical Features

Coefficients of Logistic Regression

Booking Regression

Job Recommendation

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

Perfectly Separable

Probability

First to Six

500 Cards

Raining in Seattle

Found Item

Ad Raters

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

What statistical test could you use to determine which of two parcel types is better to use, given how often they are damaged?

Different Parcel Effectiveness

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

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

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

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

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

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

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

Button AB Test

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?

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?

Bank Fraud Model

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

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

<ol>
<li>Given an example paragraph string and an integer <code>N</code>, write a function <code>n_frequent_words</code> that returns the top <code>N</code> frequent words in the posting and the frequencies for each word.</li>

<li>What’s the function run-time?</li>
</ol>

Example:

Input:

<pre tabindex="0" class="chroma"><code>posting = &#34;&#34;&#34;
Herbal sauna uses the healing properties of herbs in combination with distilled water. 
The water evaporates and distributes the effect of the herbs throughout the room. 
A visit to the herbal sauna can cause real miracles, especially for colds. 
&#34;&#34;&#34; 
n = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>n_frequent_words(posting,N) = [
 (&#39;the&#39;, 6), 
 (&#39;herbal&#39;, 2), 
 (&#39;sauna&#39;, 2),
]
</code></pre>

Attentive Interview Questions

Challenge

Attentive Salaries by Position

Job Summary: 
Attentive is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. In this role, you will own and scale AI Journeys, transforming shopper intent into high-performing, personalized consumer experiences. Responsibilities: • Design controls, guardrails, and workflows that give marketers confidence to adopt AI-driven personalization at scale • Deliver clear visibility, metrics, and reporting that explain performance, outcomes, and tradeoffs, turning automation from a black box into a trusted system • Translate complex AI decisioning into simple, intuitive marketer experiences • Build tight feedback loops with enterprise customers and internal GTM teams to ensure the product solves real-world problems and earns long-term adoption • Elevate the shopper experience with more intelligent, timely, and relevant messaging • Partner closely with engineering, data science, analytics, and design to continuously improve results, including applying generative AI and machine learning to personalize content and optimize delivery • Extend the product across channels and use cases to unlock more value for customers. Qualifications: Required: • 4+ years of product management experience • Owned products from discovery through scaling • Ability to translate customer needs into intuitive product experiences • Detail-oriented and results-driven • Ability to thrive in ambiguity and learn by doing • Experience across both backend decision logic and system architecture to user experience Preferred: • Experience building or working closely with ML and/or AI-driven systems • Past success unlocking adoption for complex products and/or optimizing consumer journeys Company: 
Attentive is a personalized mobile messaging platform that facilitates businesses with AI powered SMS and e-mail marketing solutions. Founded in 2016, the company is headquartered in Hoboken, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Job Summary: 
Attentive is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. In this role, you will own and scale AI Journeys, transforming shopper intent into high-performing, personalized consumer experiences. 
Responsibilities: 
• Design controls, guardrails, and workflows that give marketers confidence to adopt AI-driven personalization at scale • Deliver clear visibility, metrics, and reporting that explain performance, outcomes, and tradeoffs, turning automation from a black box into a trusted system • Translate complex AI decisioning into simple, intuitive marketer experiences • Build tight feedback loops with enterprise customers and internal GTM teams to ensure the product solves real-world problems and earns long-term adoption • Elevate the shopper experience with more intelligent, timely, and relevant messaging • Partner closely with engineering, data science, analytics, and design to continuously improve results, including applying generative AI and machine learning to personalize content and optimize delivery • Extend the product across channels and use cases to unlock more value for customers. 
Qualifications: 
Required: 
• 4+ years of product management experience • Owned products from discovery through scaling • Ability to translate customer needs into intuitive product experiences • Detail-oriented and results-driven • Ability to thrive in ambiguity and learn by doing • Experience across both backend decision logic and system architecture to user experience 
Preferred: 
• Experience building or working closely with ML and/or AI-driven systems • Past success unlocking adoption for complex products and/or optimizing consumer journeys 
Company: 
Attentive is a personalized mobile messaging platform that facilitates businesses with AI powered SMS and e-mail marketing solutions. Founded in 2016, the company is headquartered in Hoboken, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Job Summary: 
Attentive is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. As a Senior Machine Learning Engineer, you will build, scale, and operate production-grade ML systems that drive real-time personalization across the Attentive platform. 
Responsibilities: 
• Design, build, and operate scalable machine learning systems used in real-time targeting, decisioning, personalization systems • Own ML projects end-to-end, from data exploration and modeling to deployment, monitoring, and iteration • Proactively safeguard model and system quality through testing, monitoring, validation, and alerting • Collaborate cross-functionally with product, data, and other engineering partners to translate business problems into ML solutions • Continuously improve system reliability, performance, and engineering efficiency • Contribute to technical direction and best practices across the ML engineering team • Mentor and support other engineers through code reviews, design discussions, and knowledge sharing • Thrive in a high-impact, fast-paced, late-stage startup environment 
Qualifications: 
Required: 
• 6+ years of professional experience building production machine-learning software systems • Proven experience owning ML systems long enough to see the downstream impact of design decisions • Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or xgboost • Experience with data processing and analytics tools such as pandas, Spark, SQL, and matplotlib • Hands-on experience building automated pipelines for data processing, model training, validation, and deployment • Experience collaborating with cross-functional teams to deliver ML-powered features • Strong communication skills and a high sense of ownership 
Company: 
Attentive is a personalized mobile messaging platform that facilitates businesses with AI powered SMS and e-mail marketing solutions. Founded in 2016, the company is headquartered in Hoboken, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Job Summary: 
Attentive is the AI marketing platform for 1:1 personalization, redefining the way brands and people connect. As a Machine Learning Engineer II, you will contribute to building and improving machine learning models and infrastructure for real-time personalization across the Attentive platform, collaborating with senior engineers and product managers. 
Responsibilities: 
• Build and maintain machine learning models and pipelines used in production • Contribute to data processing, feature engineering, model training, and evaluation workflows • Assist in deploying, monitoring, and validating ML models in production environments • Partner with senior engineers and product managers to implement ML-driven features • Write high-quality, well-tested code and help prevent regressions through testing and monitoring • Learn and apply best practices in ML engineering, system reliability, and scalability • Participate in design discussions, code reviews, and team knowledge sharing • Grow your technical skills in a fast-paced, high-impact environment 
Qualifications: 
Required: 
• 3+ years of professional software engineering experience, with 2+ years working on machine learning or data-driven systems • Proficiency in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or xgboost • Experience with data analysis and processing tools such as pandas, SQL, Spark, or similar • Exposure to building or supporting ML pipelines for training, validation, or deployment • Understanding of production concerns such as monitoring, performance, and data quality • Strong collaboration and communication skills, with a growth-oriented mindset 
Company: 
Attentive is a personalized mobile messaging platform that facilitates businesses with AI powered SMS and e-mail marketing solutions. Founded in 2016, the company is headquartered in Hoboken, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

<h5>About the Role </h5>Senior Data Scientist Intelligent Messaging Our Data Science team is a world-class data, applied science, and ML organization that leads data-driven decision-making and the conversational AI efforts for the entire company. Joining our team offers a high-growth career opportunity to work with some of the worlds most talented data scientists in a high-performance and high-impact culture. As a Senior Data Scientist on our Intelligent Messaging team, you will play a crucial role in transforming data into actionable insights and models for our AI product offerings. We are looking for an experienced scientist who relishes the opportunity to develop novel approaches and apply them at Attentives scale. Your contributions will directly influence the development of cutting-edge ML solutions, empowering our team to craft highly effective messaging strategies. Attentive is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. Were the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights. Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carters work with us to enable billions of interactions that power tens of billions in revenue for our customers. With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentives team has been consistently recognized for its performance and culture. Were proud to be included in Deloittes Fast 500 (four years running!), LinkedIns Top Startups, Forbes Cloud 100 (five years running!), and Inc.s Best Workplaces. <h5>What You'll Accomplish </h5><ul> <li>Lead the development of statistical, econometric, optimization, and machine learning models for a range of applications in our messaging and AI product offerings </li><li>Design and execute experiments, interpret results, and draw actionable conclusions </li><li>Use data to understand product performance, identify improvement opportunities, and define product/team roadmaps </li><li>Present findings to senior leadership to inform and influence business decisions </li><li>Collaborate with cross-functional teams across product, engineering, client services, and sales to drive business value from ideation to production </li></ul> <h5>Your Expertise </h5><ul> <li>Ph.D., M.S. or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields </li><li>Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics </li><li>Knowledge of experimental design and analysis </li><li>Experience with exploratory data analysis, statistical analysis and testing, and model development </li><li>Ability to use Python to work efficiently at scale with large data sets </li><li>Advanced proficiency in SQL </li></ul> <h5>Nice to Haves </h5><ul> <li>4+ years of industry experience as an Applied or Data Scientist or equivalent </li><li>Familiarity with advanced statistical inference, experimentation, and causal inference methods </li><li>Econometric experience with pricing and promotion optimization </li><li>Expertise in customer lifetime value modeling </li><li>Experience in personalization systems and cross-sectional cohort modeling </li><li>Background in operations research (e.g., scheduling or assignment problems) </li><li>Knowledge of advanced topics in XGBoost modeling </li></ul> <h5>Perks </h5>You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work. <h5>For US based applicants </h5>- The US base salary range for this full-time position is $250,000 - $300,000 annually + equity + benefits - Our salary ranges are determined by role, level and location <h5>Attentive Company Values </h5>Default to Action - Move swiftly and with purpose Be One Unstoppable Team - Rally as each others champions Champion the Customer - Our success is defined by our customers' success Act Like an Owner - Take responsibility for Attentives success Learn more about AWAKE, Attentives collective of employee resource groups. <h5>Equal Opportunity </h5>Attentive is an Equal Opportunity Employer and we welcome applicants from all backgrounds. Our policy is to provide equal employment opportunities for all employees, applicants and covered individuals regardless of protected characteristics. We prioritize and maintain a fair, inclusive and equitable workplace free from discrimination, harassment, and retaliation. Attentive is committed to providing reasonable accommodations for candidates with disabilities. If you need any assistance or reasonable accommodations, please let your recruiter know. #J-18808-Ljbffr

<h5>Lead Product Manager, Data Platform Experience </h5>Attentive is the AI-powered mobile marketing platform transforming the way brands personalize consumer engagement. Attentive enables marketers to craft tailored journeys for every subscriber, driving higher recurring revenue and maximizing campaign performance. Activating real-time data from multiple channels and advanced AI, the platform personalizes content, tone, and timing to deliver 1:1 messages that truly resonate. With a top-rated customer success team recognized on G2, Attentive partners with marketers to provide strategic guidance and optimize SMS and email campaigns. Trusted by leading global brands like Neiman Marcus, Samsung, Wayfair, and Dyson, Attentive ensures enterprise-grade compliance and deliverability, supporting trillions of interactions across more than 70 industries. To learn more, visit www.attentive.com. Attentive's growth has been recognized by Deloitte's Fast 500, Linkedin's Top Startups, and Forbes Cloud 100 all thanks to the hard work from our global employees! <h5>What You'll Accomplish </h5><ul> <li>Lead the evolution of the Enhanced Attentive Tag (EAT) into a fully self-service product experience empowering customers and internal teams to configure, monitor, and troubleshoot their tagging setup with ease </li><li>Define the company-wide alerting and observability strategy, setting the foundation for how Attentive detects, communicates, and resolves data issues across tagging, catalog, and downstream systems </li><li>Deliver a reimagined UI experience that gives marketers transparent visibility into how their data flows through Attentive's platform transforming invisible infrastructure into intuitive, actionable insights </li><li>Partner with Engineering and Design leadership to build scalable internal tooling that reduces Support Engineering load and accelerates root-cause diagnosis for data issues </li><li>Collaborate with Customer Success, GTM, and Support teams to operationalize data transparency, ensuring customers have confidence in the accuracy and reliability of the data powering their campaigns </li></ul> <h5>Your Expertise </h5><ul> <li>57 years of experience in product management, ideally working on platform, data infrastructure, or observability products at a SaaS company </li><li>Comfortable engaging deeply with technical concepts from onsite data collection to event instrumentation and data pipelines to alerting and monitoring systems and can translate them into clear, user-facing solutions </li><li>Design-minded and care deeply about product usability looking for ways to simplify, visualize, and clarify how systems work </li><li>Strategic communicator who can drive alignment across Engineering, Design, and GTM teams, balancing user experience goals with platform reliability and scalability </li><li>Data-driven and measure success through adoption, reliability, and user trust, not just feature delivery </li><li>Thrive in ambiguous, cross-functional environments, where success requires connecting technical depth with customer empathy </li><li>You have experience with marketing or analytics platforms, or have previously worked on data observability, tagging, or event tracking systems </li></ul> You'll get competitive perks and benefits from health care to home office equipment, to help you bring your best self to work! For US based applicants: <ul> <li>The standard base salary range for this position is $190,000-225,000 annually </li><li>This position is eligible for equity in the form of RSUs </li></ul> Attentive Company Values <ul> <li>Default to Action - Move swiftly and with purpose </li><li>Be One Unstoppable Team - Rally as each others champions </li><li>Champion the Customer - Our success is defined by our customers success </li><li>Act Like an Owner - Take responsibility for Attentives success </li></ul> Learn more about AWAKE, Attentive's collective of employee resource groups. If you do not meet all the requirements listed here, we still encourage you to apply! No job description is perfect, and we may also have another opportunity that closely matches your skills and experience. At Attentive, we know that our companys strength lies in the diversity of our employees. Attentive is an Equal Opportunity Employer and we welcome applicants from all backgrounds. Our policy is to provide equal employment opportunities for all employees, applicants and covered individuals regardless of protected characteristics. We prioritize and maintain a fair, inclusive and equitable workplace free from discrimination, harassment, and retaliation. Attentive is also committed to providing reasonable accommodations for candidates with disabilities. If you need any assistance or reasonable accommodations, please let your recruiter know.

Attentive Opening Jobs

Prepare for and practice Attentive interview questions with this guide from Interview Query.

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