Criteo Data Analyst Interview Guide Overview

Criteo is a global commerce media company that enables marketers and media owners to deliver richer consumer experiences through its pioneering Commerce Media Platform. Renowned for its data-driven culture, Criteo operates on a massive scale, partnering with thousands of customers and publishers worldwide to drive superior marketing performance and measurable results.

As a Data Analyst at Criteo, you'll work closely with both commercial and technical teams to transform vast datasets into actionable insights that boost business performance. This role involves challenging analytical work, from conducting complex A/B tests to automating analyses with tools like Python and SQL.

In this guide, Interview Query will take you through the intricacies of Criteo’s interview process, including commonly asked questions and preparation tips. Get ready to step into a role where your data analytics skills will significantly impact global commerce strategies. Let's dive in!

Submitting Your Application

The first step in applying for a Data Analyst position at Criteo involves submitting a well-crafted application that highlights your technical skills, experience, and enthusiasm for the role. Whether you were contacted by a recruiter or have independently applied, ensure that your resume and cover letter are tailored to match the job description and requirements. Keywords and phrases relevant to the position should be included to increase the chances of your application being noticed.

Recruiter/Hiring Manager Call Screening

Once your application is shortlisted, a recruiter from Criteo will schedule a phone call to discuss your background, motivations, and alignment with the role. This initial screening typically focuses on your skills, previous experience, and your interest in the company. The recruiter may also assess your salary expectations and overall fit for the Criteo culture.

Technical Virtual Interview

If you pass the initial screen, you will be invited to a technical virtual interview. This interview is typically conducted over a video call and lasts around 45 minutes to an hour. You can expect detailed questions on SQL, Python, and general aptitude.

Additionally, you might be tested on machine learning principles, such as linear regression, and be given a business case study to solve. This stage may also include competency questions related to your ability to analyze data and derive actionable insights.

Take-Home Case Study

Following the virtual interview, you may receive a take-home assignment. This could involve analyzing a dataset using your preferred tools and presenting your findings. You will have a few days to complete this assignment, which is designed to assess your technical skills, analytical thinking, and presentation abilities.

Onsite Interview Rounds

If you clear the take-home assignment, the next stage often involves multiple onsite interview rounds. These rounds can include:

1. Technical Rounds: Focused on SQL, Python, and scenario-based questions, these sessions aim to dig deeper into your technical expertise and problem-solving skills.
2. Behavioral Interviews: These sessions will evaluate your cultural fit within the company, your interpersonal skills, and your ability to work within a team.
3. Case Study Presentation: You will present the case study and analysis. The panel may include members from the team you are applying to join. You will be asked follow-up questions to discuss your methodology and findings.

Final Interview

The final interview round is often with a senior member of the team or upper management. This session may include a mix of technical, behavioral, and situational questions to ensure that you are the right fit for the role and the company.

Quick Tips For Criteo Data Analyst Interviews

Three tips for acing your interview with Criteo include:

• Be Ready for Business Cases: Criteo’s interviews focus heavily on case studies and real-world data problems. Practice presenting data, formulating insights, and communicating your thought process clearly.
• Master Technical Skills: Brush up on SQL, Python, and basic machine learning concepts. You can use Interview Query to practice SQL queries and other data-related tasks.
• Understand Criteo’s Industry: Research Criteo’s products, data-driven advertising technology, and market trends. Being well-versed in these areas can give you an edge during the interviews.

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

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you set up this test?

• Would you suspect anything unusual if an A/B test with 20 variants shows one significant result? Your manager ran an A/B test with 20 different variants and found one significant result. Would you think there was anything fishy about the results?

• Why might the average number of comments per user decrease despite user growth in a new city? A social media company sees a slow decrease in the average number of comments per user from January to March in a new city, despite consistent user growth. What are some reasons for this decrease, and what metrics would you look into?

• What metrics would you use to determine the value of each marketing channel for a B2B analytics company? Given all the different marketing channels and their respective costs at a company called Mode, which sells B2B analytics dashboards, what metrics would you use to determine the value of each marketing channel?

• How would you locate a mouse in a 4x4 grid using the fewest number of scans? You have a 4x4 grid with a mouse trapped in one of the cells. You can "scan" subsets of cells to know if the mouse is within that subset but not its exact location. How would you figure out where the mouse is using the fewest number of scans?

• Create a function find_bigrams to return a list of all bigrams in a sentence. Write a function called find_bigrams that takes a sentence or paragraph of strings and returns a list of all its bigrams in order. A bigram is a pair of consecutive words.

• Write a query to get the last transaction for each day from a table of bank transactions. Given a table of bank transactions with columns id, transaction_value, and created_at, write a query to get the last transaction for each day. The output should include the id, datetime, and transaction amount, ordered by datetime.

• Create a function find_change to find the minimum number of coins for a given amount. Write a function find_change to find the minimum number of coins that make up the given amount of change cents. Assume we only have coins of value 1, 5, 10, and 25 cents.

• Write a function to simulate drawing balls from a jar based on their counts. Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

• Develop a function calculate_rmse to compute the root mean squared error. Write a function calculate_rmse to calculate the root mean squared error of a regression model. The function should take in two lists, one representing the predictions y_pred and another with the target values y_true.

• What is the expected number of good ads rated by different types of raters?
• Suppose we have 100 raters each rating one ad independently. What's the expected number of good ads?
• Now suppose we have 1 rater rating 100 ads. What's the expected number of good ads?

• Suppose we have 1 ad, rated as bad. What's the probability the rater was lazy?

• Write a function to simulate coin tosses with a given probability of heads. Create a function that takes the number of tosses and the probability of heads as input and returns a list of randomly generated results ('H' for heads, 'T' for tails).

Example 1: python tosses = 5 probability_of_heads = 0.6 Output: python coin_toss(tosses, probability_of_heads) -> ['H', 'T', 'H', 'H', 'T']

Example 2: python tosses = 3 probability_of_heads = 0.2 Output: python coin_toss(tosses, probability_of_heads) -> ['T', 'T', 'T']

• Write a function to calculate the sample variance of a list of integers. Create a function that takes a list of integers and returns the sample variance, rounded to 2 decimal places.

Example: python test_list = [6, 7, 3, 9, 10, 15] Output: python get_variance(test_list) -> 13.89

• What is the probability of rolling at least one 3 with dice?
• What's the probability of rolling at least one 3 with 2 dice?

• What's the probability of rolling at least one 3 given (N) dice?

• What is the probability of finding an item on Amazon's website given its availability in warehouses? Given that the probability of item X being available at warehouse A is 0.6 and at warehouse B is 0.8, what is the probability that item X would be found on Amazon's website?

• What kind of model did the co-worker develop for loan approval? Your co-worker developed a model that takes customer inputs and returns if a loan should be given or not. What kind of model is this?

• How would you measure the difference between two credit risk models? Given that personal loans are monthly installments, how would you measure the difference between two credit risk models within a timeframe?

• What metrics would you track to measure the success of a new credit risk model? What metrics would you track to measure the success of a new model predicting loan defaults?

• What metrics would you use to track the accuracy and validity of a spam classifier? You have built a V1 of a spam classifier for emails. What metrics would you use to track its accuracy and validity?

• What are the key differences between classification models and regression models? Explain the key differences between classification models and regression models.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? Provide an example of the tradeoffs between the two.

• What happens when you run logistic regression on perfectly linearly separable data? You are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

What is the average salary for a Data Analyst at Criteo?

Q: What can I expect from the interview process for the Data Analyst position at Criteo? The interview process at Criteo typically includes multiple stages: an initial phone screen with HR, an online skills assessment, a technical interview, and a case study presentation. Throughout the process, you will be assessed on your analytical skills, technical proficiency in SQL, Python, and machine learning, as well as your cultural fit with the company.

Q: What are some technical skills required for a Data Analyst at Criteo? As a Data Analyst at Criteo, you are expected to have strong skills in SQL, Python, and statistical analysis. Experience with data visualization tools like Tableau and knowledge of machine learning concepts are also beneficial. Familiarity with large datasets and understanding of internet advertising technologies (such as DSP, SSP, RTB) are a plus.

Q: What is the company culture like at Criteo? Criteo prides itself on a diverse and inclusive culture that values creativity, innovation, and teamwork. The company supports an open and collaborative environment where employees can learn from each other and grow professionally. Criteo is committed to ensuring that all employees are treated equally and do not tolerate any form of discrimination or harassment.

Q: Why should I consider working at Criteo? Criteo offers an exciting and dynamic work environment with opportunities for career advancement. You will get to work with one of the world's largest datasets and cutting-edge technologies. The company provides comprehensive benefits, career growth opportunities, and the chance to have a meaningful impact on the open internet through innovative advertising solutions.

Q: How can I best prepare for my interview at Criteo? To prepare for your interview at Criteo, it's crucial to brush up on your technical skills, particularly in SQL and Python. Practice case studies and be ready to discuss your past experiences and problem-solving approaches. Research Criteo's business model and be familiar with internet advertising technologies. Using platforms like Interview Query for practice can be very helpful.

If you're seeking in-depth insights and thorough preparation for a Data Analyst position at Criteo, our Criteo Interview Guide is your ultimate resource. We've compiled extensive interview questions, covered various aspects of the process, and created specialized guides for different roles. Whether you're looking to ace the technical rounds or understand the cultural fit, Interview Query has all the tools you need.

At Interview Query, we empower you to navigate the complex interview landscape with confidence. Our comprehensive resources are designed to equip you with the knowledge and strategic guidance to conquer Criteo's interview challenges.

Explore all our company interview guides for a thorough preparation, and feel free to reach out if you have any questions.

Good luck with your interview!