Klaviyo Data Scientist Interview Questions + Guide in 2025

Klaviyo is a leading platform that empowers creators by making first-party data accessible and actionable, enabling personalized experiences across e-commerce and beyond.

As a Data Scientist at Klaviyo, you will play a critical role in leveraging data to inform product decisions and enhance customer relationships. Key responsibilities include collaborating with cross-functional teams to design and implement data-driven algorithms, conducting experiments to optimize user engagement, and developing predictive models using statistical techniques. You will also be expected to have a strong foundation in statistical methods, machine learning, and programming—particularly in Python and SQL—as you work with large datasets hosted on AWS. A successful candidate will demonstrate excellent communication skills, an eagerness to learn, and the ability to think critically about complex problems. Klaviyo values innovation and collaboration, so being a team player who can contribute to a dynamic environment is essential.

This guide aims to equip you with the insights needed to prepare effectively for your interview, helping you showcase your skills and alignment with Klaviyo's mission and values.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Klaviyo. The interview process will likely focus on your statistical knowledge, programming skills, and ability to apply data science techniques to real-world problems. Be prepared to discuss your experience with machine learning, experimental design, and data manipulation, as well as your ability to communicate complex concepts clearly.

Statistics and Probability

1. Can you explain the difference between Type I and Type II errors?

Understanding the implications of these errors is crucial in statistical hypothesis testing, especially in a data-driven environment like Klaviyo.

How to Answer

Discuss the definitions of both errors and provide examples of situations where each might occur. Emphasize the importance of balancing the risks of these errors in decision-making.

Example

"Type I error occurs when we reject a true null hypothesis, while Type II error happens when we fail to reject a false null hypothesis. For instance, in an A/B test for a marketing campaign, a Type I error could lead to implementing a campaign that is actually ineffective, while a Type II error might result in missing out on a successful campaign."

2. How would you approach designing an A/B test?

A/B testing is a fundamental technique for evaluating the effectiveness of changes in a product or service.

How to Answer

Outline the steps involved in designing an A/B test, including defining the hypothesis, selecting the sample size, and determining the metrics for success.

Example

"I would start by clearly defining the hypothesis we want to test, such as whether a new email subject line increases open rates. Next, I would determine the sample size needed for statistical significance, ensuring that we randomly assign users to either the control or treatment group. Finally, I would track key metrics like conversion rates and analyze the results using appropriate statistical tests."

3. What is the Central Limit Theorem and why is it important?

This theorem is a cornerstone of statistical inference and is essential for understanding sampling distributions.

How to Answer

Explain the theorem and its implications for making inferences about population parameters based on sample statistics.

Example

"The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is important because it allows us to make inferences about population parameters using sample data, which is fundamental in hypothesis testing and confidence interval estimation."

4. How do you handle missing data in a dataset?

Dealing with missing data is a common challenge in data science.

How to Answer

Discuss various strategies for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

"I typically assess the extent and pattern of missing data first. If the missingness is random, I might use mean or median imputation. For larger gaps, I could consider predictive modeling techniques to estimate missing values. In some cases, if the missing data is substantial and could bias results, I may choose to exclude those records entirely."

5. Explain the concept of p-values and their significance in hypothesis testing.

P-values are a critical component of statistical testing and understanding them is essential for data scientists.

How to Answer

Define p-values and explain their role in determining the statistical significance of results.

Example

"A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A smaller p-value suggests stronger evidence against the null hypothesis. Typically, a p-value of less than 0.05 is considered statistically significant, indicating that we can reject the null hypothesis."

Machine Learning

1. What is the difference between supervised and unsupervised learning?

Understanding these concepts is fundamental for any data scientist.

How to Answer

Define both types of learning and provide examples of algorithms used in each.

Example

"Supervised learning involves training a model on labeled data, where the outcome is known, such as regression and classification tasks. In contrast, unsupervised learning deals with unlabeled data, aiming to find patterns or groupings, like clustering and dimensionality reduction techniques."

2. Can you explain overfitting and how to prevent it?

Overfitting is a common issue in machine learning that can lead to poor model performance.

How to Answer

Discuss the concept of overfitting and various techniques to mitigate it.

Example

"Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, resulting in poor generalization to new data. To prevent overfitting, I use techniques such as cross-validation, regularization, and pruning decision trees, as well as ensuring that the model complexity is appropriate for the amount of training data available."

3. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Provide a brief overview of the project, the challenges encountered, and how you addressed them.

Example

"I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced classes, as most customers did not churn. I addressed this by using techniques like SMOTE for oversampling the minority class and adjusting the classification threshold to improve recall without sacrificing precision."

4. What metrics would you use to evaluate a classification model?

Choosing the right metrics is crucial for assessing model performance.

How to Answer

Discuss various metrics and when to use them based on the problem context.

Example

"I would consider metrics such as accuracy, precision, recall, and F1-score. For imbalanced datasets, I would prioritize recall to ensure we capture as many positive cases as possible. Additionally, I would use the ROC-AUC curve to evaluate the model's performance across different thresholds."

5. How do you ensure model interpretability?

Model interpretability is increasingly important in data science, especially in customer-facing applications.

How to Answer

Discuss techniques and tools you use to make models interpretable.

Example

"I ensure model interpretability by using simpler models when possible, such as linear regression or decision trees. For more complex models, I utilize techniques like SHAP values or LIME to explain individual predictions. This helps stakeholders understand the model's decision-making process and builds trust in the results."

Here are some tips to help you excel in your interview.

Understand the Interview Process

Klaviyo's interview process typically involves multiple stages, including a take-home assignment, phone screenings, and technical interviews. Familiarize yourself with this structure and prepare accordingly. Be ready to discuss your take-home assignment in detail, as it often serves as a basis for further discussions. Given the feedback from candidates, it’s crucial to manage your expectations regarding response times, as they can be slower than anticipated.

Showcase Your Statistical Knowledge

As a Data Scientist at Klaviyo, a strong foundation in statistics is essential. Be prepared to discuss concepts such as A/B testing, sampling, and regression analysis. Candidates have noted that interviewers often focus on statistical methods and their applications, so be ready to explain your thought process and how you would approach various statistical problems. Practice articulating complex statistical concepts in a way that is accessible to non-technical stakeholders, as this is a valuable skill in collaborative environments.

Be Proficient in Python and Data Manipulation

Klaviyo operates primarily in Python, so ensure you are comfortable with data manipulation libraries such as Pandas and NumPy. Candidates have reported that coding exercises often involve data aggregation and statistical testing, so practice writing clean, efficient code in Jupyter Notebooks. Familiarize yourself with common data manipulation tasks, as well as how to visualize data effectively. Being able to demonstrate your coding skills in real-time during the interview can set you apart.

Prepare for Behavioral Questions

Klaviyo values collaboration and communication, so expect behavioral questions that assess your ability to work in teams and handle challenges. Reflect on past experiences where you successfully collaborated with cross-functional teams, navigated conflicts, or mentored others. Be ready to discuss how you approach problem-solving and decision-making in a team context, as this aligns with Klaviyo's emphasis on partnership and teamwork.

Emphasize Your Passion for Learning

Klaviyo seeks candidates who are excited about continuous learning and adapting to new challenges. Highlight your willingness to learn new technologies and methodologies, especially in areas like machine learning and causal inference. Share examples of how you have pursued professional development in the past, whether through formal education, online courses, or personal projects. This will resonate well with Klaviyo's culture of growth and innovation.

Be Ready for Technical Challenges

Expect to face technical challenges that test your problem-solving abilities. Candidates have mentioned coding exercises that require debugging or optimizing existing code. Practice common coding problems and be prepared to explain your reasoning as you work through them. Additionally, familiarize yourself with Klaviyo's tech stack, including AWS and PySpark, as this knowledge can demonstrate your readiness to contribute from day one.

Communicate Clearly and Confidently

Throughout the interview, communicate your thoughts clearly and confidently. Klaviyo values candidates who can articulate their ideas and reasoning effectively. Practice explaining your projects and technical concepts in a concise manner, as this will help you make a strong impression. Remember to engage with your interviewers, ask questions, and show genuine interest in the role and the company.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Data Scientist role at Klaviyo. Good luck!

The interview process for a Data Scientist role at Klaviyo is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the collaborative and innovative environment of the company. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial phone screening with a recruiter. This conversation usually lasts about 30 minutes and focuses on your background, experiences, and motivations for applying to Klaviyo. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role, allowing you to gauge if it aligns with your career goals.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a take-home coding exercise or a data manipulation task using Python, typically submitted in a Jupyter Notebook. The assessment is designed to evaluate your proficiency in statistical methods, data analysis, and programming skills. Expect questions that require you to demonstrate your understanding of data structures, statistical significance, and basic machine learning concepts.

3. Technical Interview

After successfully completing the technical assessment, candidates will participate in a technical interview, usually conducted via video conferencing. This interview is often led by a senior data scientist and focuses on your approach to problem-solving, coding skills, and statistical knowledge. Be prepared to discuss your previous projects, explain your thought process, and tackle questions related to probability, experimental design, and data interpretation.

4. Onsite Interview

The final stage typically involves an onsite interview, which may consist of multiple rounds with various team members, including data scientists, product managers, and engineers. These interviews will cover a mix of technical and behavioral questions, assessing your ability to work collaboratively and communicate effectively. You may be asked to solve real-world problems, discuss your previous work experiences, and demonstrate your understanding of Klaviyo's products and data science applications.

Throughout the interview process, Klaviyo emphasizes the importance of cultural fit and collaboration, so be prepared to showcase your interpersonal skills and your enthusiasm for working in a team-oriented environment.

As you prepare for your interviews, consider the types of questions that may arise in each stage of the process.

1. Write a query to get the average order value by gender.

Given three tables representing customer transactions and customer attributes, write a query to get the average order value by gender. We’re looking at the average order value of users who have ever placed an order. Round your answer to two decimal places.

2. Write a function combinational_dice_rolls to dump all possible combinations of dice rolls.

Given n dice each with m faces, write a function combinational_dice_rolls to dump all possible combinations of dice rolls. Bonus: Can you do it recursively?

3. Estimate the annual cost of overlapping computing jobs.

Every night between 7 pm and midnight, two computing jobs from different sources are randomly started, each lasting an hour. When they overlap, it causes a failure costing $1000. Write a function to simulate this problem and output an estimated annual cost. Bonus: How would you solve this using probability?

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

Create a function to generate a sample from a standard normal distribution.

5. Write a function sort_lists to merge sorted integer lists while maintaining order.

Given a list of sorted integer lists, write a function sort_lists to create a combined list while maintaining sorted order without importing any libraries or using the ‘sort’ or ‘sorted’ functions in Python.

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

Explain the concept of a p-value in simple terms to a non-technical person. Focus on its role in determining the significance of results in experiments or tests.

7. Can you determine if an AB test with unbalanced sample sizes will result in a bias towards the smaller group?

Analyze an AB test with one variant having 50K users and the other having 200K users. Determine if the unbalanced sample sizes could lead to bias towards the smaller group.

8. How would you infer if the results of a landing page redesign AB test were statistically significant?

A landing page redesign is tested via an AB test to improve click-through rates. Explain how you would determine if the results are statistically significant.

9. Is the difference in average order value between men and women significant?

You have average order value (AOV) data separated by gender: Men have an AOV of $46.3 with 2500 purchases, and Women have an AOV of $50.2 with 3500 purchases. Would the difference in AOV be significant?

10. What are the assumptions of linear regression?

Based on interview experiences, here are a few tips to excel in your Klaviyo Data Scientist interview:

1. Understand and Practice Core Concepts: Make sure you grasp probability, statistics, and coding in Python. Review concepts like Bayesian inference, sampling bias, linear regression, A/B testing, and regularization techniques.

2. Review Technical and Homework Assignments: Take-home assignments are a significant part of the interview process, so ensure your solutions are well-documented and thoroughly tested.

3. Be Prepared for Scenario-Based Questions: Klaviyo focuses on real-world data problems, so anticipate scenario-based questions where you need to think aloud, discuss your approach, and ultimately code a solution.

What is the average salary for a Data Scientist at Klaviyo?

What makes the Klaviyo Data Scientist position unique?

Klaviyo’s Data Scientist position offers the opportunity to work on a real-time data analytics platform built for a massive scale. The role involves deep involvement in technical discussions, experimentation, and optimization features to enhance user engagement. Klaviyo data scientists work collaboratively in a full-stack engineering team, gaining not only data science expertise but also software engineering skills essential for production-sizing insights.

What skills and qualifications are required for a Data Scientist at Klaviyo?

Ideal candidates should have a strong statistical background and proficiency in modern programming languages, especially Python. At least one year’s experience in data science or applied probability, a bachelor’s or advanced degree in a quantitative discipline, and the ability to communicate technical concepts clearly are essential. A passion for learning new engineering skills and working collaboratively on difficult problems is highly valued.

Interviewing for a Data Scientist position at Klaviyo can be intriguing but requires thorough preparation.

For those eager to delve deeper into Klaviyo’s interview process, be sure to check out our Klaviyo Interview Guide, where we cover a wide range of potential interview questions and share insights into the company’s specific expectations.

At Interview Query, we empower you with the knowledge, confidence, and strategic guidance to conquer every challenge in your Klaviyo data scientist interview. For more insights and tips, you can explore our comprehensive company interview guides. If you have any questions or need further assistance, please reach out.

Good luck with your interview!