Klarna Data Scientist Interview Questions + Guide in 2025

Klarna is a leading global payments provider that simplifies the purchasing experience for consumers and merchants alike.

As a Data Scientist at Klarna, you will play a crucial role in leveraging data to drive business decisions and enhance customer experiences. Your primary responsibilities will include developing predictive models to assess credit risk, conducting exploratory data analysis (EDA) to understand complex datasets, and implementing end-to-end machine learning solutions. Strong proficiency in machine learning, SQL, and Python is essential, as you will frequently work with large datasets to extract meaningful insights and make data-driven recommendations. You will also be expected to communicate effectively with cross-functional teams and stakeholders to ensure alignment on objectives and deliverables. A successful Data Scientist at Klarna will possess a strong analytical mindset, a passion for problem-solving, and a commitment to upholding the company's values of transparency and customer-centricity.

This guide aims to prepare you for your interview by providing insights into the specific skills and competencies that Klarna values in a Data Scientist, as well as the types of questions you may encounter. Being well-prepared will give you a significant advantage in showcasing your fit for the role and the company.

The interview process for a Data Scientist role at Klarna is structured and involves multiple stages designed to assess both technical and behavioral competencies.

1. Initial Assessment

The first step in the interview process is an online logical reasoning test, which typically consists of a series of questions aimed at evaluating your analytical thinking and problem-solving abilities. Candidates are usually given a set time to complete this test, and it serves as a preliminary filter to gauge basic cognitive skills.

2. HR Screening Interview

Following the logical test, candidates are invited to a 30-minute screening call with a member of the HR team. This conversation is generally informal and focuses on discussing the candidate's background, motivations, and understanding of the role. It’s also an opportunity for candidates to ask questions about the company culture and the specifics of the position.

3. Technical Assessment

Candidates who pass the HR screening are then assigned a technical task, which often involves a machine learning case study. This task requires candidates to perform exploratory data analysis (EDA), build a machine learning model, and deploy the solution on a cloud platform. The complexity of this task can vary, but it typically demands a significant investment of time and effort, often exceeding 10 hours.

4. Supervised Logical Test

After completing the technical assessment, candidates may be required to take another logical reasoning test, this time under supervision via video call. This step is designed to ensure the integrity of the assessment process and to further evaluate the candidate's logical reasoning skills.

5. Technical Interview

Candidates who successfully navigate the previous stages will participate in a technical interview, which usually involves discussions about the completed technical task. Interviewers may ask candidates to explain their thought process, the methodologies used, and the results obtained. This interview may also include questions on machine learning concepts, algorithms, and coding challenges.

6. Behavioral Interview

The final stage of the interview process is a behavioral interview, where candidates are asked a series of questions aimed at understanding their interpersonal skills, work ethic, and alignment with Klarna's values. This interview often includes situational questions that assess how candidates would handle specific challenges in the workplace.

As you prepare for your interview, it’s essential to familiarize yourself with the types of questions that may arise during these stages.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Klarna. The interview process will assess your technical skills in machine learning, statistics, and data analysis, as well as your problem-solving abilities and cultural fit within the company. Be prepared to discuss your past projects, demonstrate your analytical thinking, and showcase your understanding of the financial technology landscape.

Machine Learning

1. Can you explain the process of building a machine learning model from start to finish?

Understanding the end-to-end process of machine learning is crucial for this role, as it demonstrates your ability to handle projects independently.

How to Answer

Outline the steps you take, from data collection and preprocessing to model selection, training, evaluation, and deployment. Highlight any specific tools or frameworks you prefer.

Example

“I typically start with data collection and cleaning, ensuring that I handle missing values and outliers appropriately. Then, I explore the data to understand its structure and relationships. After selecting a suitable model, I train it using cross-validation techniques and evaluate its performance using metrics like accuracy or AUC. Finally, I deploy the model to a cloud service for real-time predictions.”

2. How do you handle imbalanced datasets in a classification problem?

Imbalanced datasets are common in financial applications, such as fraud detection or credit risk modeling.

How to Answer

Discuss techniques like resampling, using different evaluation metrics, or applying algorithms that are robust to class imbalance.

Example

“To address imbalanced datasets, I often use techniques like SMOTE for oversampling the minority class or undersampling the majority class. Additionally, I focus on metrics like F1-score or ROC-AUC instead of accuracy to better evaluate model performance.”

3. Describe a machine learning project you worked on and the challenges you faced.

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Choose a project that highlights your technical skills and the impact of your work. Discuss specific challenges and how you overcame them.

Example

“In my last project, I developed a credit risk model. One major challenge was dealing with a highly imbalanced dataset. I implemented SMOTE to balance the classes and used ensemble methods to improve prediction accuracy. The final model reduced false negatives by 20%, significantly improving our risk assessment process.”

4. What is your experience with deploying machine learning models?

Deployment is a critical aspect of the data science workflow, especially in a production environment.

How to Answer

Discuss your familiarity with cloud platforms and any specific tools you’ve used for deployment.

Example

“I have experience deploying models using AWS and Azure. I typically use Docker containers to ensure consistency across environments and leverage services like AWS Lambda for serverless deployment, which allows for scalable and cost-effective solutions.”

5. How do you ensure the reproducibility of your machine learning experiments?

Reproducibility is essential in data science to validate results and facilitate collaboration.

How to Answer

Mention practices like version control, documentation, and using environments that can be replicated.

Example

“I ensure reproducibility by using version control systems like Git for my code and maintaining detailed documentation of my experiments. I also use virtual environments to manage dependencies, which allows others to replicate my work easily.”

Statistics & Probability

1. Explain the Central Limit Theorem and its significance.

A solid understanding of statistical concepts is vital for data analysis and interpretation.

How to Answer

Define the theorem and discuss its implications in practical scenarios.

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 original distribution. This is significant because it allows us to make inferences about population parameters even when the population distribution is unknown.”

2. What is the difference between Type I and Type II errors?

Understanding errors in hypothesis testing is crucial for making informed decisions based on data.

How to Answer

Clearly define both types of errors and provide examples to illustrate your points.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, in a credit risk model, a Type I error might mean incorrectly classifying a low-risk applicant as high-risk, leading to lost business opportunities.”

3. How do you approach feature selection in your models?

Feature selection is key to improving model performance and interpretability.

How to Answer

Discuss methods you use for feature selection and the rationale behind them.

Example

“I use techniques like recursive feature elimination and LASSO regression to identify the most important features. Additionally, I analyze feature importance scores from tree-based models to ensure that I’m including only the most relevant variables in my final model.”

4. Can you explain what p-values represent?

P-values are fundamental in hypothesis testing, and understanding them is essential for data scientists.

How to Answer

Define p-values and discuss their role in statistical significance.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

5. What is the purpose of A/B testing, and how do you conduct it?

A/B testing is a common method for evaluating changes in products or services.

How to Answer

Explain the process of A/B testing and its importance in data-driven decision-making.

Example

“A/B testing allows us to compare two versions of a product to determine which performs better. I start by defining clear metrics for success, then randomly assign users to each group. After collecting data, I analyze the results using statistical tests to determine if the observed differences are significant.”

SQL & Data Manipulation

1. Write a SQL query to find the top 10 customers by total spending.

SQL skills are essential for data extraction and manipulation.

How to Answer

Provide a clear and efficient SQL query that addresses the question.

Example

“SELECT customer_id, SUM(spending) AS total_spending FROM transactions GROUP BY customer_id ORDER BY total_spending DESC LIMIT 10;”

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

Handling missing data is a common challenge in data analysis.

How to Answer

Discuss various strategies for dealing with missing values.

Example

“I handle missing data by first assessing the extent of the missingness. Depending on the situation, I might impute missing values using the mean or median, or I may choose to drop rows or columns with excessive missingness. I always ensure to document my approach for transparency.”

3. Explain the difference between INNER JOIN and LEFT JOIN in SQL.

Understanding SQL joins is crucial for data manipulation and analysis.

How to Answer

Define both types of joins and provide examples of when to use each.

Example

“An INNER JOIN returns only the rows that have matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. For instance, if I want to list all customers and their orders, I would use a LEFT JOIN to ensure that customers without orders are still included in the results.”

4. How would you optimize a slow-running SQL query?

Performance optimization is key in data-heavy environments.

How to Answer

Discuss techniques you use to improve query performance.

Example

“To optimize a slow-running SQL query, I would start by analyzing the execution plan to identify bottlenecks. I might add indexes to frequently queried columns, rewrite the query to reduce complexity, or break it into smaller, more manageable parts.”

5. Can you explain window functions in SQL and provide an example?

Window functions are powerful tools for data analysis in SQL.

How to Answer

Define window functions and describe their use cases.

Example

“Window functions allow us to perform calculations across a set of rows related to the current row. For example, I can use the ROW_NUMBER() function to assign a unique rank to each customer based on their total spending within their respective regions, which is useful for comparative analysis.”