Yammer, Inc. Data Analyst Interview Questions + Guide in 2025

Yammer, Inc. is a dynamic social networking service and collaboration platform that empowers organizations to enhance their internal communication and teamwork.

The Data Analyst role at Yammer, Inc. involves leveraging data to drive informed decision-making and improve business outcomes. Key responsibilities include analyzing complex datasets, generating actionable insights, and presenting findings to stakeholders. The ideal candidate possesses a strong foundation in statistics and probability, demonstrates proficiency in data visualization tools, and exhibits the ability to communicate technical concepts to non-technical audiences. Candidates should also be adept at problem-solving, critical thinking, and possess a keen attention to detail. A successful Data Analyst at Yammer will thrive in a collaborative environment and align with the company’s commitment to innovation and excellence in enhancing workplace collaboration.

This guide will help you prepare for your job interview by providing insights into the expectations and types of questions you may encounter, ensuring you can effectively showcase your skills and fit for the role.

The interview process for a Data Analyst position at Yammer, Inc. is structured to assess both technical skills and cultural fit within the company. The process typically unfolds in several key stages:

1. Online Assessment

Candidates begin with a 20-minute online assessment that consists of six questions, primarily focused on math and statistics. This initial test is designed to evaluate your quantitative skills and ability to apply analytical thinking to real-world scenarios. Expect a mix of straightforward math problems and a general question that may require a short answer.

2. Recruiter Phone Screen

Following the online assessment, candidates will have a 30-minute phone conversation with a recruiter. This discussion will cover your resume, clarify the job duties, and explore your motivations for applying to Yammer. The recruiter will also assess your fit for the company culture and your overall interest in the role.

3. Technical Phone Interview

Next, candidates will participate in a 30-minute technical phone interview with a junior data analyst. This interview will delve deeper into your analytical skills, featuring more math-related questions and scenario-based inquiries. Be prepared to discuss how you would evaluate data-driven applications or features, as well as to solve brain teasers that test your problem-solving abilities.

4. Onsite Interviews

The final stage consists of five onsite interviews, each lasting approximately 45 minutes. These interviews will involve various team members, including senior analysts and directors. Expect a mix of technical questions, brain teasers, and real-world data analysis scenarios. Interviewers will challenge you with questions that require you to think critically and apply your knowledge to practical situations, such as how to instrument everyday objects or evaluate new applications.

Throughout the process, candidates should be ready to demonstrate their analytical thinking, problem-solving skills, and ability to communicate complex ideas clearly.

Now, let's explore the specific interview questions that candidates have encountered during this process.

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

Understand the Interview Structure

Yammer's interview process typically includes multiple stages, starting with a recruiter call, followed by a phone screen, and culminating in several on-site interviews. Familiarize yourself with this structure so you can prepare accordingly. Expect a mix of technical questions, brain teasers, and scenario-based discussions. Knowing what to expect will help you manage your time and energy throughout the process.

Prepare for Math and Probability Questions

Given the emphasis on math and statistics in the interview process, it’s crucial to brush up on these areas. Practice solving problems related to probability, statistics, and data interpretation. Be ready to explain your thought process clearly, as interviewers will be interested in how you approach problem-solving rather than just the final answer. Consider using online resources or textbooks to refresh your knowledge and practice sample questions.

Embrace Scenario-Based Questions

Yammer values practical application of data analysis skills. Prepare for scenario-based questions where you may need to discuss how you would approach real-world problems using data. For example, you might be asked how to evaluate a new application feature or how to instrument a common household item. Think critically about how you would gather data, measure success, and analyze results in these scenarios.

Hone Your Communication Skills

Effective communication is key in a data analyst role, especially when discussing complex data insights with non-technical stakeholders. Practice articulating your thoughts clearly and concisely. During the interview, ensure you explain your reasoning and methodologies in a way that is accessible to all interviewers, regardless of their technical background.

Prepare for Brain Teasers

Expect to encounter brain teasers during your interviews. These questions are designed to assess your analytical thinking and problem-solving abilities. Familiarize yourself with common brain teasers and practice solving them. Resources like online forums or puzzle books can be helpful. Remember, the goal is to demonstrate your thought process, so take your time and think aloud as you work through these challenges.

Showcase Your Passion for Data

Yammer is looking for candidates who are not only skilled but also passionate about data analysis. Be prepared to discuss your previous experiences, projects, or any personal initiatives that demonstrate your enthusiasm for the field. Share specific examples of how you have used data to drive decisions or improve processes in past roles.

Align with Company Culture

Yammer values collaboration and innovation. Research the company culture and think about how your values align with theirs. Be ready to discuss how you work in teams, handle feedback, and contribute to a positive work environment. Showing that you understand and fit into their culture can give you a significant advantage in the interview process.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Analyst role at Yammer. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Analyst interview at Yammer, Inc. The interview process will likely assess your analytical skills, problem-solving abilities, and understanding of statistical concepts. Be prepared to discuss real-world applications of data analysis and demonstrate your critical thinking through various scenarios.

Mathematical and Statistical Skills

1. Can you explain the difference between variance and standard deviation?

Understanding these concepts is crucial for any data analyst, as they are fundamental in measuring data dispersion.

How to Answer

Clearly define both terms and explain their significance in data analysis. Highlight how they can be used to interpret data sets.

Example

"Variance measures how far a set of numbers is spread out from their average value, while standard deviation is the square root of variance, providing a measure of dispersion in the same units as the data. Both are essential for understanding the reliability of data and making informed decisions based on that data."

2. How would you approach a situation where you have missing data?

This question assesses your problem-solving skills and your ability to handle real-world data issues.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

"I would first analyze the extent and pattern of the missing data. If it's minimal, I might consider deletion. For larger gaps, I would explore imputation methods, such as mean or median substitution, or use predictive models to estimate missing values, ensuring that the integrity of the dataset is maintained."

3. Describe a time when you had to analyze a large dataset. What tools did you use?

This question evaluates your practical experience with data analysis tools and techniques.

How to Answer

Mention specific tools and methodologies you used, and describe the outcome of your analysis.

Example

"In my previous role, I analyzed a dataset of over a million customer transactions using SQL for data extraction and Python for data manipulation. I utilized Pandas for data cleaning and visualization libraries like Matplotlib to present my findings, which helped the marketing team tailor their campaigns effectively."

4. What statistical tests would you use to compare two groups?

This question tests your knowledge of statistical methods and their applications.

How to Answer

Identify the appropriate tests based on the data type and distribution, and explain your reasoning.

Example

"I would typically use a t-test if the data is normally distributed and the sample sizes are small. For larger samples or non-normally distributed data, I would opt for a Mann-Whitney U test. The choice depends on the data characteristics and the specific hypothesis being tested."

Data Interpretation and Application

5. How would you instrument a toaster to gather data?

This question assesses your ability to think critically about data collection in everyday scenarios.

How to Answer

Discuss the types of data you would collect and how you would implement the instrumentation.

Example

"I would install temperature sensors to monitor the heating elements and a timer to track cooking duration. Additionally, I would consider user interaction data, such as button presses, to analyze usage patterns and improve the design based on user preferences."

6. Suppose you are a manager at Facebook. How would you evaluate a new application feature?

This question evaluates your ability to apply data analysis in a business context.

How to Answer

Outline the metrics you would use to assess the feature's performance and the methods for data collection.

Example

"I would define key performance indicators (KPIs) such as user engagement rates, retention rates, and user feedback scores. I would implement A/B testing to compare the new feature against the existing one, analyzing the data to determine its impact on user experience and overall satisfaction."

7. Can you describe a challenging data analysis problem you faced and how you solved it?

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

How to Answer

Provide a specific example, detailing the problem, your approach, and the outcome.

Example

"I once faced a challenge with a dataset that had numerous outliers affecting the results. I conducted an exploratory data analysis to identify the outliers and used robust statistical methods, such as median and interquartile range, to minimize their impact. This approach led to more accurate insights and actionable recommendations for the team."

8. What methods do you use to ensure data quality?

This question assesses your understanding of data integrity and quality assurance processes.

How to Answer

Discuss the techniques and best practices you follow to maintain high data quality.

Example

"I implement data validation checks at the point of entry, conduct regular audits, and use automated scripts to identify anomalies. Additionally, I ensure proper documentation and version control to track changes and maintain data integrity throughout the analysis process."