Surveymonkey Research Scientist Interview Questions + Guide in 2025

Surveymonkey provides innovative survey solutions that empower individuals and organizations to gather, analyze, and act on feedback, making data-driven decisions easier and more accessible.

The Research Scientist role at Surveymonkey is pivotal in enhancing the understanding of user experiences and refining data products. Key responsibilities include designing and conducting experiments, analyzing complex datasets, and developing algorithms that drive insights into user behavior and product success. Candidates should be proficient in algorithms and have a solid foundation in Python, as well as experience with statistical analysis and machine learning concepts. A strong analytical mindset, the ability to translate data findings into actionable recommendations, and excellent communication skills are essential to thrive in this role. Understanding the company's commitment to user-centric solutions is crucial, as it aligns with the expectations for data-driven research that informs product development and strategy.

This guide will equip you with the knowledge and insights needed to prepare effectively for your interview, helping you demonstrate your alignment with Surveymonkey's values and the specific requirements of the Research Scientist role.

The interview process for a Research Scientist at Surveymonkey is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the role. The process typically unfolds in several stages:

1. Initial Phone Screen

The first step is a phone screening with a recruiter, which usually lasts about 30 minutes. During this call, the recruiter will discuss your background, experience, and motivations for applying to Surveymonkey. This is also an opportunity for you to ask questions about the company culture and the specifics of the Research Scientist role.

2. Technical Screening

Following the initial screen, candidates typically undergo a technical interview, which may be conducted via a coding platform or through a live coding session. This interview focuses on your proficiency in algorithms, Python, and SQL, as well as your ability to solve analytical problems. Expect to tackle questions that assess your understanding of data manipulation and statistical methods relevant to research.

3. Case Study or Project Presentation

Candidates may be required to present a case study or a project they have previously worked on. This stage allows you to showcase your analytical skills and your ability to communicate complex ideas effectively. Be prepared to discuss the methodologies you used, the challenges you faced, and the outcomes of your project.

4. Onsite Interviews

The onsite interview typically consists of multiple rounds, often involving 4 to 5 interviews with various team members, including engineers, product managers, and possibly senior leadership. Each interview lasts around 45 minutes and covers a mix of technical questions, system design discussions, and behavioral questions. Interviewers will assess your problem-solving skills, your approach to research, and how well you fit within the team dynamics.

5. Final Interview

The final stage may include a conversation with a senior manager or VP, focusing on cultural fit and your long-term career aspirations. This is a chance for you to express your interest in the company and discuss how your values align with Surveymonkey's mission.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and past experiences.

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

Understand the Company Culture

SurveyMonkey values a collaborative and supportive work environment. Familiarize yourself with their mission and values, and be prepared to discuss how your personal values align with theirs. Highlight experiences where you contributed to team success or fostered a positive work atmosphere. This will demonstrate that you are not only a skilled candidate but also a cultural fit.

Prepare for Technical Proficiency

As a Research Scientist, you will likely face questions that assess your understanding of algorithms, Python, and SQL. Brush up on your algorithmic knowledge, focusing on common data structures and their complexities. Practice coding problems that require you to implement algorithms in Python, as this is a key skill for the role. Additionally, be ready to discuss your experience with SQL, particularly in data manipulation and querying.

Showcase Your Analytical Skills

Expect to encounter questions that evaluate your analytical thinking and problem-solving abilities. Be prepared to discuss past projects where you utilized statistical methods or data analysis techniques. Highlight your experience with probability and analytics, and be ready to explain how you approached complex problems and derived insights from data.

Engage in Behavioral Questions

Behavioral questions are a significant part of the interview process. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on your past experiences and prepare examples that showcase your strengths, such as teamwork, leadership, and adaptability. Be honest and authentic in your responses, as SurveyMonkey values transparency and integrity.

Communicate Clearly and Confidently

During the interview, articulate your thoughts clearly and confidently. If you encounter a challenging question, take a moment to think before responding. It’s perfectly acceptable to ask for clarification if you don’t understand a question. Demonstrating your thought process can be just as important as arriving at the correct answer.

Prepare for Case Studies and Practical Exercises

You may be asked to complete a case study or practical exercise during the interview. Familiarize yourself with common case study frameworks and practice analyzing data sets or designing experiments. Be ready to discuss your thought process and the rationale behind your decisions. This will showcase your ability to apply theoretical knowledge to real-world scenarios.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity. Reiterate your interest in the position and briefly mention a key point from the interview that resonated with you. This not only shows your professionalism but also keeps you top of mind for the interviewers.

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

In this section, we’ll review the various interview questions that might be asked during a Research Scientist interview at SurveyMonkey. The interview process will likely focus on your analytical skills, experience with data science methodologies, and your ability to communicate complex ideas effectively. Be prepared to discuss your past projects, technical skills, and how you approach problem-solving in a research context.

Experience and Background

1. Describe a project you worked on that involved data analysis. What were the key successes and challenges?

This question aims to assess your practical experience and ability to reflect on your work.

How to Answer

Discuss a specific project, highlighting your role, the methodologies used, and the outcomes. Emphasize both successes and challenges to show your ability to learn and adapt.

Example

“I worked on a project analyzing customer feedback data to improve product features. We successfully identified key areas for enhancement, which led to a 20% increase in user satisfaction. However, we faced challenges in data cleaning, which taught me the importance of robust preprocessing steps.”

Machine Learning

2. What advantages would using a Bayesian method for hypothesis testing have over a Frequentist one?

This question tests your understanding of statistical methodologies relevant to research.

How to Answer

Explain the differences between Bayesian and Frequentist approaches, focusing on the flexibility and interpretability of Bayesian methods.

Example

“Bayesian methods allow for the incorporation of prior knowledge and provide a probabilistic interpretation of results, which can be more intuitive for decision-making. This flexibility can be particularly useful in research settings where prior data is available.”

3. How do you validate a machine learning model?

This question assesses your knowledge of model evaluation techniques.

How to Answer

Discuss various validation techniques such as cross-validation, train-test splits, and performance metrics.

Example

“I validate machine learning models using k-fold cross-validation to ensure robustness. I also monitor metrics like accuracy, precision, and recall, depending on the problem type, to assess model performance comprehensively.”

4. Can you explain the concept of overfitting and how to prevent it?

This question evaluates your understanding of model training and generalization.

How to Answer

Define overfitting and discuss techniques to mitigate it, such as regularization and cross-validation.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like L1/L2 regularization and ensure to validate the model on unseen data.”

Data Analysis and SQL

5. Describe your experience with SQL. What types of queries have you written?

This question gauges your technical skills in data manipulation.

How to Answer

Provide examples of SQL queries you’ve written, focusing on complexity and the types of data you’ve worked with.

Example

“I have extensive experience with SQL, including writing complex joins, aggregate functions, and subqueries to extract insights from large datasets. For instance, I created a query to analyze user engagement metrics across different demographics.”

6. How do you handle missing data in a dataset?

This question assesses your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation or removal.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. Depending on the situation, I may use imputation techniques like mean or median substitution or, if the missing data is substantial, consider removing those records to maintain data integrity.”

Problem-Solving and Critical Thinking

7. Give an example of a time you used data to make a decision.

This question evaluates your analytical thinking and decision-making process.

How to Answer

Describe a specific instance where data analysis influenced a decision, detailing the data used and the outcome.

Example

“In a previous role, I analyzed user behavior data to determine the effectiveness of a new feature. The data indicated low engagement, leading us to pivot our strategy and enhance the feature based on user feedback, resulting in a 30% increase in usage.”

8. How would you approach designing an experiment to test a new product feature?

This question tests your understanding of experimental design.

How to Answer

Outline the steps you would take, including defining objectives, selecting metrics, and determining sample size.

Example

“I would start by clearly defining the hypothesis and objectives of the experiment. Next, I would select relevant metrics to measure success, such as user engagement or conversion rates, and determine an appropriate sample size to ensure statistical significance.”

Communication and Collaboration

9. How do you communicate complex data findings to non-technical stakeholders?

This question assesses your ability to convey information effectively.

How to Answer

Discuss strategies for simplifying complex concepts and using visual aids.

Example

“I focus on using clear, non-technical language and visual aids like charts and graphs to present data findings. I also tailor my communication style to the audience, ensuring they understand the implications of the data in their context.”

10. Describe a time you had to resolve a conflict within a team. How did you handle it?

This question evaluates your interpersonal skills and conflict resolution abilities.

How to Answer

Provide a specific example, detailing the conflict, your approach, and the resolution.

Example

“In a previous project, there was a disagreement between team members about the direction of our analysis. I facilitated a meeting where everyone could voice their concerns, and we collaboratively reached a consensus by weighing the pros and cons of each approach, ultimately leading to a more robust analysis.”