Criteo Data Scientist Interview Questions + Guide in 2025

Criteo is a global leader in personalized performance marketing, leveraging vast datasets to deliver targeted advertising solutions.

As a Data Scientist at Criteo, you will engage with complex challenges in the advertising software industry, working at a scale and speed that few others can match. Your primary responsibilities will include building and improving predictive models, gathering and analyzing data, and identifying key prediction problems to propose innovative solutions. You will also be tasked with reporting, visualizing, and communicating results effectively, while contributing to the exploration of new scientific understandings such as monitoring and developing new metrics.

To excel in this role, you should possess a strong analytical background in mathematics and machine learning, with at least 5 years of experience in the software industry. Proficiency in programming languages such as Python is essential, as well as a detail-oriented mindset and a proactive approach to problem-solving. You will thrive in Criteo's dynamic and multicultural environment, which emphasizes empowerment, agility, and collaboration among team members.

This guide will help you prepare for a job interview by providing insights into the expectations for the Data Scientist role at Criteo, the skills and experiences that are valued, and the types of questions you may encounter during the interview process.

The interview process for a Data Scientist role at Criteo is structured and involves multiple stages designed to assess both technical skills and cultural fit.

1. Initial Screening

The process typically begins with an initial screening call with a recruiter. This conversation lasts about 30 minutes and focuses on your background, motivations for applying to Criteo, and your proficiency in English. The recruiter aims to gauge your fit for the company culture and your enthusiasm for the role.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a take-home project where you analyze a dataset relevant to Criteo's business model. You will need to present your findings in a follow-up interview, which may include technical questions related to your analysis, machine learning algorithms, and statistical methods.

3. Technical Interviews

Candidates typically undergo two or more technical interviews conducted via video conferencing. These interviews focus on your understanding of machine learning concepts, statistical analysis, and programming skills. Expect questions that assess your knowledge of algorithms, data structures, and practical applications of data science in real-world scenarios. You may also encounter brain teasers or market sizing questions to evaluate your problem-solving abilities.

4. Team Fit and Cultural Interview

The final stage usually involves interviews with team members, including a data team lead and other data scientists. These interviews assess your fit within the team and the broader Criteo culture. Questions may revolve around your past experiences, how you handle challenges, and your approach to collaboration. This stage is crucial as Criteo values a multicultural and agile work environment.

5. Final Review

After completing the interviews, there may be a final review stage where the interviewers discuss your performance and fit for the role. This is often followed by feedback from HR regarding the outcome of your application.

As you prepare for your interviews, it's essential to familiarize yourself with the types of questions that may be asked during the process.

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

Understand Criteo's Business Model

Familiarize yourself with Criteo's unique position in the advertising technology landscape. Understand how their algorithms work, particularly in relation to performance marketing and retargeting. Be prepared to discuss how your skills can contribute to optimizing their existing models and potentially innovating new solutions. This knowledge will not only help you answer questions more effectively but also demonstrate your genuine interest in the company.

Prepare for Technical Assessments

Given the emphasis on technical skills, ensure you are well-versed in statistics, machine learning algorithms, and programming languages relevant to the role, particularly Python. Review key concepts such as logistic regression, time series analysis, and handling missing data. Practice coding problems that reflect the types of challenges you might face at Criteo, including data manipulation and analysis tasks.

Embrace the Brain Teasers

Criteo interviews often include brain teasers and market sizing questions. These are designed to assess your problem-solving abilities and how you approach complex scenarios. Practice common brain teasers and develop a structured approach to tackle them. Think aloud during the interview to show your thought process, as interviewers appreciate seeing how you arrive at your conclusions.

Showcase Your Analytical Skills

During the interview, be prepared to discuss your past experiences with data analysis and how you have used data to drive decisions. Highlight specific projects where you identified key prediction problems and proposed innovative solutions. Use metrics and results to quantify your impact, as this aligns with Criteo's focus on data-driven decision-making.

Communicate Clearly and Confidently

Criteo values clear communication, especially when discussing complex technical topics. Practice explaining your past projects and technical concepts in a way that is accessible to non-experts. This will be particularly important during the fit interviews, where you may need to articulate your thought process and collaborate with team members.

Be Ready for Cultural Fit Questions

Criteo has a vibrant and multicultural work environment. Be prepared to discuss how you can contribute to their culture of empowerment, agility, and collaboration. Reflect on your experiences working in diverse teams and how you adapt to different working styles. Show enthusiasm for the opportunity to engage with colleagues from various backgrounds.

Follow Up with Questions

At the end of your interview, take the opportunity to ask insightful questions about the team dynamics, ongoing projects, and the company's future direction. This not only shows your interest in the role but also helps you gauge if Criteo is the right fit for you. Tailor your questions to reflect your understanding of their business and culture.

By following these tips, you will be well-prepared to make a strong impression during your interview at Criteo. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Criteo. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the company. Be prepared to discuss your experience with machine learning, statistics, and data analysis, as well as your understanding of Criteo's business model.

Machine Learning

1. Which machine learning algorithms are you most familiar with, and can you explain how they work?

Understanding various algorithms is crucial for a Data Scientist role, especially in a company focused on predictive modeling.

How to Answer

Discuss a few algorithms in detail, focusing on their applications and how they differ from one another. Highlight any specific experiences you've had with these algorithms.

Example

“I have extensive experience with decision trees, random forests, and logistic regression. For instance, I used random forests to improve the accuracy of a customer segmentation model, which allowed us to target marketing efforts more effectively.”

2. How would you approach building a predictive model for ad click-through rates (CTR)?

This question assesses your ability to apply machine learning to real-world problems relevant to Criteo.

How to Answer

Outline the steps you would take, from data collection to model evaluation. Emphasize the importance of feature selection and validation.

Example

“I would start by gathering historical data on ad impressions and clicks, then perform exploratory data analysis to identify key features. After preprocessing the data, I would use logistic regression to model CTR, validating the model with cross-validation techniques to ensure robustness.”

3. Can you explain the concept of overfitting and how to prevent it?

Overfitting is a common issue in machine learning, and understanding it is essential for model performance.

How to Answer

Define overfitting and discuss techniques to mitigate it, such as regularization and cross-validation.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like L1 and L2 regularization and ensure to validate the model on a separate test set.”

4. Describe a time when you had to optimize a machine learning model. What steps did you take?

This question evaluates your practical experience in model optimization.

How to Answer

Share a specific example, detailing the challenges faced and the methods used to improve the model's performance.

Example

“I worked on a recommendation system where the initial model had a low accuracy. I optimized it by tuning hyperparameters using grid search and incorporated additional features based on user behavior, which significantly improved the model's performance.”

Statistics & Probability

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

Handling missing data is a critical skill for data analysis.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation or removal, and when to use each.

Example

“I typically assess the extent of missing data first. If it's minimal, I might use mean imputation. For larger gaps, I prefer to use predictive modeling techniques to estimate missing values based on other features.”

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

Understanding statistical errors is fundamental for hypothesis testing.

How to Answer

Define both types of errors and provide examples of their implications in a business context.

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. In a marketing context, a Type I error could mean incorrectly concluding that a campaign is effective when it is not, leading to wasted resources.”

3. What statistical tests would you use to compare two groups?

This question assesses your knowledge of statistical methods.

How to Answer

Mention specific tests and the scenarios in which you would apply them.

Example

“I would use a t-test to compare the means of two groups if the data is normally distributed. If not, I would opt for a non-parametric test like the Mann-Whitney U test.”

4. How would you explain the concept of p-value to a non-technical audience?

Being able to communicate complex concepts simply is vital in a collaborative environment.

How to Answer

Use analogies or simple language to explain the concept.

Example

“A p-value helps us understand the strength of our evidence against the null hypothesis. If we have a low p-value, it suggests that the observed data is unlikely under the null hypothesis, indicating that our findings are statistically significant.”

Data Analysis

1. Describe your experience with SQL and how you use it in data analysis.

SQL skills are essential for data manipulation and retrieval.

How to Answer

Discuss your proficiency with SQL and provide examples of complex queries you've written.

Example

“I have extensive experience with SQL, including writing complex joins and subqueries to extract insights from large datasets. For instance, I created a query that aggregated user behavior data to identify trends in ad performance.”

2. How do you visualize data to communicate findings effectively?

Data visualization is key to presenting insights clearly.

How to Answer

Mention tools and techniques you use for data visualization and why they are effective.

Example

“I often use tools like Tableau and Matplotlib to create visualizations. For example, I created a dashboard that visualized user engagement metrics over time, which helped stakeholders quickly grasp trends and make informed decisions.”

3. Can you walk us through a data analysis project you completed?

This question allows you to showcase your analytical skills and project experience.

How to Answer

Provide a structured overview of the project, including objectives, methods, and outcomes.

Example

“I worked on a project analyzing customer churn. I gathered data from various sources, performed exploratory analysis to identify key factors, and built a logistic regression model to predict churn. The insights led to targeted retention strategies that reduced churn by 15%.”

4. How do you ensure the quality and integrity of your data?

Data quality is crucial for accurate analysis.

How to Answer

Discuss your approach to data validation and cleaning.

Example

“I implement data validation checks at the point of entry and regularly audit datasets for inconsistencies. Additionally, I use automated scripts to clean and preprocess data before analysis, ensuring high integrity.”