Klaviyo Data Scientist Interview Questions + Guide in 2024

Klaviyo is a dynamic company specializing in data-driven marketing. It provides businesses with tools to harness the power of real-time data. Built on a real-time analytics platform primarily coded in Python and hosted on AWS, Klaviyo supports scalability and in-depth analytics.

For the Data Scientist position, Klaviyo seeks individuals with a robust statistical background and proficiency in at least one modern programming language. The role involves working with various statistical methods, focusing on real-time data processing, probability, and modeling. A strong interest in collaborative projects with software engineers, product managers, and designers is crucial.

In this guide, Interview Query will walk you through the interview process, provide insights into the commonly asked Klaviyo data scientist interview questions you might face, and offer tips on preparing effectively. Dive in to begin your journey with Klaviyo!

The interview process usually depends on the role and seniority; however, you can expect the following on a Klaviyo data scientist interview:

Recruiter/Hiring Manager Call Screening

If your application is shortlisted, you will be contacted by a recruiter from Klaviyo’s Talent Acquisition Team. The recruiter will verify key details such as your work experiences and skill level and discuss the role further. Behavioral questions may also come up during this screening process.

Sometimes, the data science hiring manager may join this call to answer your queries regarding the role and Klaviyo as a company. They might also delve into some surface-level technical and behavioral discussions during this 30-minute call.

Initial Phone Interview

You will be invited to an initial phone interview if you pass the initial call screening. This stage usually involves basic questions about your background. You may expect questions about your resume, technical skills, and experience with data science projects.

Technical Virtual Interview

Moving forward from the initial interview, you will be invited to a technical screening round conducted virtually. This stage typically takes about an hour and may involve video conferencing and screen-sharing.

This interview will focus on your statistical knowledge, probability, coding, and problem-solving skills. Questions during this stage may include probability calculations, sampling methods, and newsletter response rates.

A common format for this round is first to discuss the problem and proceed to whiteboarding solutions, followed by a coding implementation.

Take-Home Assignment

You may be given a take-home assignment to complete using a Jupyter Notebook at some point in the interview process. This assignment will likely involve tasks such as data manipulation, statistical calculations, and data visualization.

Past tasks have included:

• Assembling a data frame with customer data.
• Plotting order counts.
• Comparing mean order values for different genders.
• Constructing a confusion matrix to evaluate a prediction model.

You are expected to submit this assignment within a given timeframe and may be asked to explain your approach in subsequent interviews.

Onsite Interview Rounds

After successfully completing the take-home assignment and technical virtual interview, you will be invited to onsite interview rounds at Klaviyo’s Boston office.

These rounds generally involve meetings with multiple team members, including senior data scientists and the hiring manager. These interviews will encompass discussions on probability, statistics, linear regression, machine learning models, and case studies. You might also be required to demonstrate coding skills and solve data problems immediately.

An in-person interview might also require you to present the take-home assignment, discuss your solution, and discuss any additional projects or case studies.

Typically, interviews at Klaviyo vary by role and team, but commonly, Data Scientist interviews follow a fairly standardized process across these question topics.

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!