Criteo Research Scientist Interview Questions + Guide in 2025

Criteo is a global leader in online advertising technology, specializing in driving performance and enhancing advertisers' return on investment through data-driven solutions.

As a Research Scientist at Criteo, you will engage in pioneering machine learning research that directly impacts the digital advertising landscape. Your key responsibilities will include developing high-performance algorithms, contributing innovative ideas that shape product direction, and conducting experimental data modeling. You will work on a variety of cutting-edge topics such as click prediction, recommender systems, auction theory, and reinforcement learning. A strong candidate will possess a PhD in Machine Learning or a related field, with extensive experience in data manipulation, algorithm development, and a solid track record of academic publications. Familiarity with programming languages like Python, Java, or Scala, as well as experience with large-scale data environments, is essential.

This guide will help you prepare effectively for your interview by providing insights into the skills and knowledge required for success at Criteo, ensuring you stand out as a candidate who aligns with the company's innovative and data-driven culture.

The interview process for a Research Scientist at Criteo is designed to assess both technical expertise and cultural fit within the organization. It typically unfolds in several structured stages:

1. Initial Screening

The first step in the interview process is an initial screening, which usually takes place via a phone or video call with a recruiter. This conversation focuses on your background, research interests, and understanding of machine learning concepts. The recruiter will also gauge your fit for Criteo's culture and values, as well as discuss the specifics of the role and the expectations for a Research Scientist.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview with a member of the research team. This interview is more in-depth and focuses on your knowledge and experience in machine learning, particularly in areas relevant to Criteo's work, such as real-time bidding, recommendation systems, and experimental design. Expect to answer questions that assess your problem-solving skills and your ability to apply theoretical knowledge to practical scenarios.

3. Research Presentation

In some cases, candidates may be asked to prepare a research presentation. This step allows you to showcase your previous work, research methodologies, and findings. It’s an opportunity to demonstrate your ability to communicate complex ideas clearly and effectively, which is crucial for collaboration within the team and for contributing to the research community.

4. Final Interview

The final stage often involves a series of interviews with senior researchers and team leads. These interviews may include both technical and behavioral questions, focusing on your past experiences, your approach to research challenges, and how you would contribute to the team’s goals. This is also a chance for you to ask questions about the team dynamics, ongoing projects, and the overall vision for research at Criteo.

Throughout the process, candidates should be prepared to discuss their research in detail, including methodologies, results, and implications for the field.

Next, let’s delve into the specific interview questions that candidates have encountered during this process.

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

Understand the Research Landscape

Familiarize yourself with the latest trends and challenges in machine learning, particularly in areas relevant to Criteo's work, such as real-time bidding, recommender systems, and auction theory. Being able to discuss recent advancements or challenges in these fields will demonstrate your passion and knowledge, making you a more compelling candidate.

Prepare for Technical Depth

Given the emphasis on machine learning, ensure you can discuss your research in detail. Be ready to explain your methodologies, the data you worked with, and the outcomes of your projects. Highlight your experience with data gathering, cleaning, and modeling, as well as your proficiency in programming languages like Python. This will showcase your technical skills and your ability to contribute to Criteo's research initiatives.

Showcase Problem-Solving Skills

Criteo values innovative thinking and the ability to tackle complex problems. Prepare to discuss specific challenges you've faced in your research and how you approached them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on how your solutions had a practical impact.

Emphasize Collaboration and Communication

Criteo's culture promotes collaboration across teams. Be prepared to discuss your experiences working in teams, how you communicate complex ideas, and how you mentor others. Highlight any collaborative projects or publications, as this will demonstrate your ability to work effectively in a multicultural and agile environment.

Be Ready for Behavioral Questions

Expect questions that assess your fit within Criteo's culture, such as how you handle feedback, adapt to change, and contribute to a team. Reflect on your past experiences and be ready to share examples that illustrate your adaptability, empowerment, and commitment to continuous improvement.

Ask Insightful Questions

Prepare thoughtful questions that reflect your understanding of Criteo's mission and research goals. Inquire about the specific challenges the research team is currently facing or how they measure the impact of their work. This not only shows your interest in the role but also your proactive approach to understanding the company’s needs.

Follow Up with Gratitude

After the interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your enthusiasm for the role and briefly mention a key point from the interview that resonated with you. This leaves a positive impression and reinforces your interest in joining the team.

By following these tips, you can position yourself as a strong candidate who not only possesses the necessary technical skills but also aligns well with Criteo's innovative and collaborative culture. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Research Scientist interview at Criteo. The focus will be on machine learning concepts, research methodologies, and practical applications relevant to the company's work in online advertising and computational advertising.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Discuss the definitions of both types of learning, providing examples of algorithms used in each. Highlight the scenarios where each type is applicable.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as classification tasks using algorithms like decision trees. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering using K-means.”

2. How would you approach a click prediction problem?

This question assesses your practical application of machine learning in a real-world scenario.

How to Answer

Outline the steps you would take, including data collection, feature engineering, model selection, and evaluation metrics.

Example

“I would start by gathering historical click data, then perform feature engineering to extract relevant features such as user behavior and ad characteristics. I would choose a model like logistic regression or a gradient boosting machine, and evaluate its performance using metrics like AUC-ROC.”

3. What techniques would you use to handle imbalanced datasets?

Imbalanced datasets are common in click prediction and recommendation systems.

How to Answer

Discuss various techniques such as resampling methods, cost-sensitive learning, or using specific algorithms designed for imbalanced data.

Example

“To address imbalanced datasets, I would consider techniques like oversampling the minority class or undersampling the majority class. Additionally, I might use algorithms like SMOTE to generate synthetic samples or apply cost-sensitive learning to penalize misclassifications of the minority class more heavily.”

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

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

How to Answer

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

Example

“I worked on a recommendation system for an e-commerce platform. One challenge was dealing with cold-start users. I implemented a hybrid approach combining collaborative filtering and content-based filtering, which significantly improved recommendations for new users.”

5. How do you evaluate the performance of a machine learning model?

Understanding model evaluation is key to ensuring the effectiveness of your solutions.

How to Answer

Discuss various metrics and validation techniques, emphasizing the importance of selecting the right metric for the problem at hand.

Example

“I evaluate model performance using metrics like accuracy, precision, recall, and F1-score, depending on the problem. For click prediction, I would focus on AUC-ROC and log loss to assess the model's ability to rank predictions effectively.”

Research Methodologies

1. How do you ensure the reproducibility of your research?

Reproducibility is vital in scientific research, especially in machine learning.

How to Answer

Discuss practices such as version control, documentation, and using standardized datasets.

Example

“I ensure reproducibility by using version control systems like Git for code management, documenting my experiments thoroughly, and utilizing standardized datasets to allow others to replicate my results easily.”

2. Can you describe a time when your research led to a publication?

This question assesses your ability to contribute to the academic community.

How to Answer

Share a specific example, detailing the research process and the impact of the publication.

Example

“I conducted research on reinforcement learning algorithms for dynamic pricing, which resulted in a publication at a leading conference. The paper detailed our methodology and findings, contributing to the understanding of pricing strategies in e-commerce.”

3. What is your experience with experimental design?

Understanding experimental design is crucial for conducting valid research.

How to Answer

Discuss your approach to designing experiments, including control groups and randomization.

Example

“I have experience designing A/B tests for product features, ensuring proper randomization and control groups to minimize bias. This approach allows for accurate measurement of the impact of changes on user behavior.”

4. How do you stay current with advancements in machine learning?

This question gauges your commitment to continuous learning in a rapidly evolving field.

How to Answer

Mention specific resources, conferences, or communities you engage with to stay updated.

Example

“I regularly read journals like JMLR and attend conferences such as NeurIPS and ICML. I also participate in online forums and communities like Kaggle to learn from peers and stay informed about the latest trends and techniques.”

5. Describe a research problem you would like to explore at Criteo.

This question allows you to demonstrate your initiative and alignment with the company's goals.

How to Answer

Propose a relevant research problem, explaining its significance and potential impact on Criteo's products.

Example

“I am interested in exploring the optimization of bidding strategies in real-time auctions using reinforcement learning. This research could enhance Criteo's ad placement efficiency and improve overall campaign performance.”