Criteo Data Science Interview Questions and Answers (2026 Guide)

Criteo is one of the world's leading commerce media and digital advertising technology companies. It uses Artificial Intelligence, Machine Learning, and Data Science to deliver personalized advertising experiences across millions of users and products.

As a data-driven organization, Criteo hires Data Scientists who can solve complex business problems using analytics, experimentation, machine learning, and large-scale data processing.

If you're preparing for a Data Science role at Criteo, this guide covers some of the most commonly asked interview questions along with detailed answers.

Why Data Science is Important at Criteo

Criteo's business relies heavily on:

• Recommendation Systems

• Predictive Modeling

• User Behavior Analysis

• Click-Through Rate Prediction

• Ad Personalization

• Real-Time Bidding

• Customer Segmentation

Data Scientists help optimize advertising performance and improve user engagement.

Statistics Interview Questions

1. What is the difference between Population and Sample?

Population

The complete set of observations.

Example:

All users visiting an e-commerce website.

Sample

A subset selected from the population.

Example:

10,000 randomly selected users.

2. What is Central Limit Theorem?

The Central Limit Theorem states that as sample size increases, the distribution of sample means approaches a normal distribution regardless of the original population distribution.

This concept is heavily used in experimentation and hypothesis testing.

3. What is Statistical Significance?

Statistical significance determines whether observed results are likely due to chance.

Common threshold:

P-Value < 0.05

4. What is a Confidence Interval?

A confidence interval provides a range of values likely to contain the true population parameter.

Example:

95% Confidence Interval:
(48%, 52%)

5. What is Bias-Variance Tradeoff?

High Bias

• Underfitting

• Oversimplified model

High Variance

• Overfitting

• Sensitive to training data

The goal is to balance both for optimal model performance.

SQL Interview Questions

6. What is SQL?

SQL (Structured Query Language) is used to retrieve, analyze, and manipulate data stored in relational databases.

7. Find Top 5 Products by Revenue

SELECT product_id,
SUM(revenue) AS total_revenue
FROM sales
GROUP BY product_id
ORDER BY total_revenue DESC
LIMIT 5;

8. Difference Between INNER JOIN and LEFT JOIN

INNER JOIN

Returns matching records from both tables.

LEFT JOIN

Returns all records from the left table and matching records from the right table.

9. Find Duplicate Users

SELECT user_id,
COUNT(*)
FROM users
GROUP BY user_id
HAVING COUNT(*) > 1;

10. What are Window Functions?

Window functions perform calculations across a set of rows related to the current row.

Example:

SELECT
user_id,
RANK() OVER(
ORDER BY revenue DESC
) AS rank
FROM users;

Python Interview Questions

11. Why is Python Popular in Data Science?

Python offers powerful libraries such as:

• Pandas

• NumPy

• Scikit-Learn

• TensorFlow

• PyTorch

It simplifies data analysis and machine learning development.

12. What is a DataFrame?

A DataFrame is a tabular data structure in Pandas consisting of rows and columns.

import pandas as pd

df = pd.read_csv("users.csv")

13. Difference Between List and NumPy Array

14. How Do You Handle Missing Values?

Methods include:

• Drop Rows

• Fill Mean

• Fill Median

• Interpolation

Example:

df.fillna(df.mean())

Machine Learning Interview Questions

15. What is Supervised Learning?

Supervised Learning uses labeled data to predict outcomes.

Examples:

• Regression

• Classification

16. What is Logistic Regression?

Logistic Regression is a classification algorithm used to predict probabilities.

Common use cases:

• Click Prediction

• Customer Churn

• Fraud Detection

17. What is Random Forest?

Random Forest is an ensemble learning algorithm that combines multiple decision trees.

Advantages:

• High Accuracy

• Handles Missing Data

• Reduces Overfitting

18. What is XGBoost?

XGBoost is a powerful gradient boosting algorithm widely used in machine learning competitions and production systems.

Benefits:

• High Performance

• Fast Training

• Excellent Accuracy

19. What is Overfitting?

Overfitting occurs when a model performs well on training data but poorly on unseen data.

Solutions:

• Cross Validation

• Regularization

• More Data

• Simpler Models

Recommendation System Questions

Recommendation systems are highly relevant for companies like Criteo.

20. What is a Recommendation System?

A recommendation system suggests relevant products, services, or content to users.

Examples:

• Amazon Product Recommendations

• Netflix Movie Suggestions

• YouTube Recommendations

21. What are the Types of Recommendation Systems?

Content-Based Filtering

Recommendations based on item characteristics.

Collaborative Filtering

Recommendations based on user behavior.

Hybrid Systems

Combination of both approaches.

22. What is Matrix Factorization?

Matrix Factorization decomposes user-item interaction matrices to discover hidden relationships.

Used extensively in recommendation engines.

Advertising Analytics Questions

23. What is Click Through Rate (CTR)?

CTR measures the percentage of users who click on an advertisement.

Formula:

CTR = Clicks / Impressions × 100

24. What is Conversion Rate?

Conversion Rate measures how many users complete a desired action.

Formula:

Conversion Rate =
Conversions / Visitors × 100

25. What is Customer Lifetime Value (CLV)?

CLV estimates the total revenue a customer will generate throughout their relationship with a business.

26. What is Attribution Modeling?

Attribution Modeling determines which marketing touchpoints contribute to conversions.

Common models include:

• First Touch

• Last Touch

• Linear Attribution

A/B Testing Questions

A/B Testing is one of the most important topics at Criteo.

27. What is A/B Testing?

A/B Testing compares two versions of a product, feature, or advertisement to determine which performs better.

28. What is the Null Hypothesis?

The Null Hypothesis assumes no significant difference exists between groups.

Example:

H₀:
New Ad CTR = Old Ad CTR

29. What Metrics Would You Track During an Ad Experiment?

Common metrics include:

• CTR

• Conversion Rate

• Revenue

• Cost Per Click

• Return on Ad Spend (ROAS)

Scenario-Based Questions

30. CTR Suddenly Drops by 30%. What Would You Do?

Steps:

1. Verify tracking systems.

2. Analyze traffic sources.

3. Check ad placements.

4. Examine audience changes.

5. Review recent deployments.

6. Investigate competitors and seasonality.

31. How Would You Improve Ad Performance?

Potential approaches:

• Better Targeting

• Audience Segmentation

• Recommendation Models

• Ad Personalization

• Creative Optimization

• Bid Strategy Improvements

32. How Would You Detect Fraudulent Clicks?

Indicators include:

• Abnormally High Click Frequency

• Repeated IP Addresses

• Unusual User Behavior

• Bot Traffic Patterns

Machine Learning models can identify suspicious activity automatically.

Key Skills Required for Criteo Data Science Roles

Candidates should focus on:

Technical Skills

• Python

• SQL

• Statistics

• Machine Learning

• Deep Learning

• Recommendation Systems

Business Skills

• Digital Advertising

• Marketing Analytics

• Experimentation

• Customer Analytics

Tools

• Pandas

• NumPy

• Scikit-Learn

• TensorFlow

• PyTorch

• Spark

Tips to Crack Criteo Data Science Interviews

Master Statistics

Questions on probability, experimentation, and hypothesis testing are common.

Practice SQL Daily

Focus on:

• Joins

• Window Functions

• Aggregations

• Complex Queries

Learn Recommendation Systems

Criteo heavily relies on personalization technologies.

Understand Advertising Metrics

Be comfortable with:

• CTR

• CPC

• CPM

• Conversion Rate

• ROAS

Solve Business Problems

Interviewers often evaluate structured thinking and analytical problem-solving.

Final Thoughts

Criteo Data Science interviews typically assess a combination of machine learning knowledge, statistical expertise, SQL proficiency, experimentation skills, and business understanding.

Candidates who can combine technical excellence with practical problem-solving and advertising analytics knowledge are more likely to succeed.

Building strong foundations in Statistics, Machine Learning, Recommendation Systems, and A/B Testing will significantly improve your chances of securing a Data Science role at Criteo.