The New York Times Data Analyst Interview Questions + Guide in 2025

The New York Times is a leading global news organization that provides in-depth reporting and analysis to inform and engage readers across various platforms.

As a Data Analyst at The New York Times, you will play a vital role in transforming data into actionable insights that drive decision-making and support the organization's strategic goals. Your primary responsibilities will include analyzing large datasets to identify trends and patterns, conducting statistical analysis to inform business strategies, and collaborating with cross-functional teams to provide data-driven recommendations. Proficiency in SQL, particularly with tools such as Google BigQuery, will be crucial for aggregating and interpreting data related to website performance, user engagement, and marketing effectiveness.

In addition to strong analytical skills, a successful candidate will have a keen understanding of statistical principles such as A/B testing and sample size determination. You should be comfortable articulating complex technical concepts to non-technical stakeholders and possess excellent communication skills. A passion for data storytelling and a collaborative spirit will align well with The New York Times' commitment to innovation and accuracy in journalism.

This guide will help you prepare for your interview by providing insights into the key skills, responsibilities, and the company culture that you can expect, ensuring you present your best self throughout the process.

The interview process for a Data Analyst position at The New York Times is structured and involves several key steps designed to assess both technical skills and cultural fit.

1. Initial Phone Screen

The process typically begins with a brief phone screen conducted by a recruiter. This initial conversation lasts around 10 to 15 minutes and focuses on your background, relevant experiences, and motivation for applying to The New York Times. Expect questions that gauge your interest in the company and the role, as well as some basic HR inquiries.

2. Technical Assessment

Following the phone screen, candidates are required to complete a technical assessment, which primarily focuses on SQL skills. This assessment is often conducted via a virtual platform, such as Google BigQuery, where you will solve a series of SQL problems. The assessment is designed to evaluate your ability to manipulate data and perform queries, with a passing score typically requiring correct answers to a subset of the questions.

3. Video Interviews

Candidates who successfully pass the technical assessment will move on to a series of video interviews. These interviews may include both automated questions and live discussions with team members. You will be asked to respond to questions about your experience, skills, and specific data projects you have worked on. The format may vary, with some questions allowing for preparation time and others requiring immediate responses.

4. Onsite or Final Round Interviews

The final stage of the interview process usually consists of multiple back-to-back interviews with various team members or hiring managers. This round can be intensive, often lasting several hours and covering a range of topics, including case studies related to A/B testing, marketing campaign optimization, and metrics for measuring success. Interviewers will assess both your technical knowledge and your soft skills, including how you approach problem-solving and collaboration.

5. Follow-Up and References

After the final interviews, candidates may be asked to provide references. However, communication regarding the outcome of the interviews can vary, with some candidates reporting delays in feedback or follow-up from the recruiter.

As you prepare for your interview, consider the types of questions that may arise during this process.

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

Prepare for SQL Assessments

Given that SQL assessments are a significant part of the interview process, it’s crucial to be well-prepared. Familiarize yourself with Google BigQuery, as this is the platform used for assessments. Focus on mastering fundamental SQL concepts such as GROUP BY, JOINs, CASE statements, and window functions. Practice solving problems that involve aggregating data, filtering results, and performing calculations. Aim to be comfortable with both basic and intermediate SQL queries, as you may encounter straightforward questions that require quick thinking.

Understand the Company’s Values and Culture

The New York Times values transparency, integrity, and a commitment to quality journalism. Familiarize yourself with their mission and recent initiatives, especially those related to data analytics. Be prepared to discuss how your values align with the company’s and how you can contribute to their goals. This understanding will not only help you answer questions more effectively but also demonstrate your genuine interest in the organization.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you handle challenges. Prepare to discuss specific projects you’ve worked on, particularly those that involved data analysis. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your contributions and the impact of your work. This approach will help you convey your experience clearly and effectively.

Showcase Your Analytical Thinking

During the interview, you may be asked to explain your thought process when tackling data-related problems. Be prepared to discuss how you would approach A/B testing, measure the success of a product, or segment an audience. Articulate your reasoning and the metrics you would consider, as this will demonstrate your analytical skills and business acumen.

Engage with Interviewers

While technical skills are essential, the interview process at The New York Times also emphasizes soft skills and cultural fit. Engage with your interviewers by asking insightful questions about their work, the team dynamics, and the challenges they face. This not only shows your interest in the role but also helps you gauge if the company is the right fit for you.

Follow Up Professionally

After your interviews, send a thoughtful thank-you email to express your appreciation for the opportunity. Mention specific points from your conversation to reinforce your interest in the role and the company. This small gesture can leave a positive impression and keep you top of mind as they make their decision.

By following these tips, you’ll be well-prepared to navigate the interview process at The New York Times and showcase your qualifications as a Data Analyst. Good luck!

SQL and Data Manipulation

1. Can you explain how you would aggregate website page views by device type?

This question assesses your understanding of SQL aggregation functions and your ability to manipulate data effectively.

How to Answer

Discuss the SQL functions you would use, such as SUM() and GROUP BY, and explain how you would structure your query to achieve the desired results.

Example

"I would use the SUM() function to total the page views and GROUP BY to categorize the results by device type. The SQL query would look something like this: SELECT device_type, SUM(page_views) FROM website_data GROUP BY device_type; This would give us a clear view of how each device contributes to overall page views."

2. How would you write a SQL query to find the hour of the day when desktop and mobile views are most frequent?

This question tests your ability to work with time data and aggregate it effectively.

How to Answer

Explain how you would extract the hour from a timestamp and then aggregate the views by hour.

Example

"I would use the EXTRACT(HOUR FROM timestamp_column) function to get the hour from the timestamp. Then, I would group the results by hour and use COUNT() to find the number of views for each hour. The SQL query would look like this: SELECT EXTRACT(HOUR FROM timestamp_column) AS hour, COUNT(*) FROM views_data GROUP BY hour ORDER BY COUNT(*) DESC;"

3. Describe how you would perform an anti-join in SQL.

This question evaluates your understanding of SQL joins and data comparison.

How to Answer

Discuss the concept of anti-joins and how you would implement it using SQL.

Example

"An anti-join is used to find records in one table that do not have corresponding records in another table. I would use a LEFT JOIN combined with a WHERE clause to filter out the matches. For example: SELECT A.* FROM tableA A LEFT JOIN tableB B ON A.id = B.id WHERE B.id IS NULL; This would return all records from table A that do not exist in table B."

4. How would you handle missing data in a dataset?

This question assesses your data cleaning and preprocessing skills.

How to Answer

Explain the methods you would use to identify and handle missing data, such as imputation or removal.

Example

"I would first analyze the extent of the missing data using functions like COUNT() or IS NULL. Depending on the situation, I might choose to impute missing values using the mean or median for numerical data, or mode for categorical data. If the missing data is extensive, I might consider removing those records entirely to maintain the integrity of the analysis."

Statistics and Probability

1. What is A/B testing, and how would you set it up?

This question tests your understanding of experimental design and statistical analysis.

How to Answer

Discuss the principles of A/B testing, including control and treatment groups, and how you would measure success.

Example

"A/B testing involves comparing two versions of a webpage or product to determine which performs better. I would randomly assign users to either the control group (A) or the treatment group (B) and measure key metrics such as conversion rates. After collecting sufficient data, I would use statistical tests like a t-test to analyze the results and determine if the differences are statistically significant."

2. How do you determine the sample size needed for a study?

This question evaluates your knowledge of statistical power and sample size calculations.

How to Answer

Explain the factors that influence sample size, such as effect size, significance level, and power.

Example

"To determine the sample size, I would consider the expected effect size, the desired significance level (usually 0.05), and the power of the test (commonly set at 0.8). Using these parameters, I would apply a sample size formula or use software tools to calculate the minimum number of participants needed to detect a meaningful effect."

3. Can you explain the concept of statistical significance?

This question assesses your understanding of hypothesis testing.

How to Answer

Define statistical significance and explain its importance in data analysis.

Example

"Statistical significance indicates whether the results of a study are likely due to chance. It is typically assessed using a p-value, where a p-value less than 0.05 suggests that the observed effect is statistically significant. This helps us make informed decisions based on data rather than random fluctuations."

4. How would you segment an audience for a marketing campaign?

This question tests your ability to apply statistical methods to real-world scenarios.

How to Answer

Discuss the criteria you would use for segmentation and the data analysis techniques involved.

Example

"I would segment the audience based on demographic factors, behavior, and engagement levels. Using clustering techniques like K-means or hierarchical clustering, I could identify distinct groups within the data. This would allow for targeted marketing strategies tailored to each segment's preferences and behaviors."