Yandex Data Analyst Interview Questions + Guide in 2024

Yandex, a prominent name in the technology and internet services industry, stands out for its innovative products and research-driven approach. As a leader in search engines and digital services based in Russia, Yandex provides a vibrant and challenging work environment.

At Interview Query, we’ve crafted this guide to help you understand the interview process, along with commonly asked Yandex data analyst interview questions, to help you prepare better. Let’s dive in!

The interview process usually depends on the role and seniority. However, you can expect the following on a Yandex data analyst interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Yandex Talent Acquisition Team will contact you and verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the Yandex data analyst hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 20 minutes and may include some simple questions on math and statistics.

Technical Virtual Interview

Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the Yandex data analyst role is usually conducted virtually, including a video conference and screen sharing. Questions in this interview stage may revolve around Yandex’s data systems, ETL pipelines, and SQL queries.

In the case of data analyst roles, here’s what you can expect:

• Case Study: You might be presented with a case study related to one of Yandex’s products.
• Statistics and Probability Theory: Prepare for questions regarding hypothesis testing, probability distributions, and similar statistical concepts.
• Algorithms and Data Structures: You may be asked to solve a problem, such as finding three numbers in a list that sum to a specific value, using programming languages like Python.
• Machine Learning Fundamentals: Questions might include understanding the trade-offs between True Positive Rate (TPR) and False Positive Rate (FPR), gradient descent equations, and risk estimations. Tasks could involve coding, for instance, calculating the ROC AUC and implementing it in any language.
• General Questions: About motivations, experience, and methodology of testing hypotheses in different conditions.

Up to three people might question you during this stage, each focusing on a specific area.

Onsite Interview Rounds

Following a second recruiter call outlining the next stage, you’ll be invited to attend the on-site interview loop. During your day at the Yandex office, multiple interview rounds, varying with the role, will be conducted.

Prepare for the following:

• Team Interviews: You might have interviews with people from your prospective feature team, where members join at different stages.
• Technical Questions: In-depth questions about algorithms, data structures, SQL, and machine learning might be asked.
• Behavioral Questions: You’ll be questioned about your experiences, motivations, and approach to specific scenarios. Brainteasers and Case Studies: You may encounter brainteasers or case studies, such as devising a methodology to test a hypothesis under different conditions.

Candidates have found the interviewers to be friendly and polite, contributing to a positive interview atmosphere despite the nervousness of the multi-interviewer setup.

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

1. 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.

2. 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.

3. Develop 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.

4. Create a function to simulate drawing balls from a jar.

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.

5. Write a function calculate_rmse to calculate the root mean squared error of a regression model.

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

6. What is the probability that it’s actually raining in Seattle given your friends’ responses?

You are about to get on a plane to Seattle and want to know if you should bring an umbrella. You call 3 random friends who live there and ask each independently if it’s raining. Each friend has a ²⁄₃ chance of telling the truth and a ¹⁄₃ chance of lying. All 3 friends tell you “Yes” it is raining. What is the probability that it’s actually raining in Seattle?

7. 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?

8. 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?

9. 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?

10. What metrics would you use to determine the value of each marketing channel for a B2B company?

Given all the different marketing channels and their respective costs at a company selling B2B analytics dashboards, what metrics would you use to determine the value of each marketing channel?

11. 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. How would you figure out where the mouse is using the fewest number of scans?

12. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle coefficients.

13. What kind of model did the co-worker develop for loan approval?

Identify the type of model used for determining loan approval based on customer inputs.

14. How would you evaluate the suitability of a decision tree for predicting loan repayment?

Describe the criteria and methods you would use to determine if a decision tree algorithm is appropriate for predicting loan repayment.

15. How does random forest generate the forest and why use it over logistic regression?

Describe the process by which a random forest generates its ensemble of trees and explain the advantages of using random forest over logistic regression.

16. How would you interpret coefficients of logistic regression for categorical and boolean variables?

Explain the interpretation of logistic regression coefficients when dealing with categorical and boolean variables.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Yandex data analyst interview include:

• Brush Up on Technical Skills: Make sure you are well-prepared for questions on SQL, algorithms, data structures, and machine learning.
• Practice Case Studies and Brainteasers: Go through various case studies, and understand how to devise methodologies for testing hypotheses. Practice solving brainteasers too.
• Understand Yandex Products: Research Yandex’s products thoroughly and understand fundamental concepts that could be relevant during your interview.

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

According to Glassdoor, Data Analyst at Yandex earn between $82K to $126K per year, with an average of $102K per year.

How many interviewers will be present, and how should I prepare for that?

There are usually around three interviewers, each questioning you in their own way, making the atmosphere intense but stimulating. Sometimes, additional team members may join the different stages of the interview. It is beneficial to practice through platforms like Interview Query for a smoother experience.

What should I expect in terms of non-technical questions?

Non-technical questions usually involve discussing your motivation, past experiences, and understanding your problem-solving approach. You’ll also encounter brainteasers and case studies assessing your hypothesis-testing methodology and critical thinking skills.

What is the company culture like at Yandex?

The company culture at Yandex is collaborative and supportive. Interviewers are generally friendly and polite, which helps create a comfortable environment for candidates to showcase their skills and suitability for the role.

The interview process for the Data Analyst position at Yandex is comprehensive, offering a thorough evaluation of your technical skills and problem-solving capabilities. From the initial phone screen with HR to the technical deep dives with team leads and feature teams, you’ll be tested on a range of topics including statistics, probability theory, algorithms, data structures, SQL, and machine learning. Each stage involves challenges like case studies on Yandex products, coding tasks, and conceptual questions to assess your analytical thinking and methodological approach to hypothesis testing under various conditions.

If you want more insights about the company, check out our main Yandex Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as data analyst, where you can learn more about Yandex’s interview process for different positions.

Good luck with your interview!