Grammarly Data Scientist Interview Questions + Guide in 2025

Grammarly is a leading technology company that leverages AI and human expertise to enhance communication across various platforms.

As a Data Scientist at Grammarly, you will play a pivotal role in driving growth and improving user experience through data-driven insights. This position involves defining innovative metrics to measure the success of products, collaborating with cross-functional teams to design and implement experiments, and developing machine learning models that optimize user engagement. You will analyze large datasets to uncover actionable insights, ensuring that these findings inform strategic decisions across product and marketing teams. A successful candidate will have a strong background in data analysis, statistics, and experimentation, as well as proficiency in programming languages such as SQL and Python. The ideal Data Scientist embodies Grammarly’s EAGER values—being ethical, adaptable, gritty, empathetic, and remarkable—while demonstrating excellent communication and analytical skills.

This guide is designed to equip you with insights and strategies to excel in your interview for the Data Scientist role at Grammarly, helping you to articulate your experience and align your skills with the company’s innovative and collaborative culture.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Grammarly. The interview process will likely assess your technical skills, analytical thinking, and ability to communicate complex ideas effectively. Be prepared to discuss your past experiences, case studies, and how you approach problem-solving in a collaborative environment.

Experience and Background

1. Can you walk us through a project where you used data to drive business decisions?

This question aims to understand your practical experience and how you leverage data in real-world scenarios.

How to Answer

Focus on a specific project, detailing your role, the data you analyzed, the insights you derived, and how those insights influenced business decisions.

Example

“In my previous role, I led a project analyzing user engagement data for a mobile app. By segmenting users based on their activity levels, I identified key features that drove retention. My findings led to a redesign of the onboarding process, which increased user retention by 20% over three months.”

Machine Learning

2. Describe a machine learning model you developed. What was the problem, and how did you approach it?

This question assesses your technical expertise and problem-solving skills in machine learning.

How to Answer

Discuss the problem you were addressing, the data you used, the model you chose, and the results you achieved. Highlight any challenges you faced and how you overcame them.

Example

“I developed a predictive model to forecast customer churn for a subscription service. I used logistic regression, trained on historical user data, and implemented feature engineering to improve accuracy. The model achieved an accuracy of 85%, allowing the marketing team to target at-risk customers effectively.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics and their importance.

How to Answer

Explain the metrics you use to evaluate models, such as accuracy, precision, recall, F1 score, or AUC-ROC, and why they are relevant to the specific problem.

Example

“I typically use accuracy and F1 score for classification problems, as they provide a balance between precision and recall. For instance, in a fraud detection model, I prioritize recall to ensure we catch as many fraudulent cases as possible, even if it means sacrificing some precision.”

Statistics & Probability

4. Explain the concept of A/B testing and how you would design an experiment.

This question evaluates your knowledge of experimental design and statistical analysis.

How to Answer

Discuss the steps you would take to design an A/B test, including defining the hypothesis, selecting metrics, and ensuring randomization.

Example

“To design an A/B test for a new feature, I would first define the hypothesis, such as ‘The new feature will increase user engagement by 15%.’ Next, I would select key metrics like session duration and conversion rates. I’d ensure random assignment of users to control and treatment groups to eliminate bias, and run the test for a sufficient duration to gather statistically significant results.”

5. What statistical methods do you use to handle missing data?

This question assesses your understanding of data preprocessing techniques.

How to Answer

Discuss various methods for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I often use mean or median imputation for numerical data, depending on the distribution. For categorical data, I might use the mode or create a separate category for missing values. In some cases, I prefer to use algorithms like Random Forest that can handle missing data without imputation.”

Data Analysis

6. How do you approach cleaning and preparing data for analysis?

This question evaluates your data wrangling skills and attention to detail.

How to Answer

Outline your process for data cleaning, including identifying and handling outliers, missing values, and ensuring data consistency.

Example

“I start by exploring the dataset to identify missing values and outliers. I use visualizations to understand distributions and relationships. After that, I handle missing values through imputation or removal, standardize formats, and ensure consistency across categorical variables before proceeding with analysis.”

7. Describe a time when you had to present complex data insights to a non-technical audience. How did you ensure they understood?

This question assesses your communication skills and ability to simplify complex information.

How to Answer

Share an experience where you successfully communicated data insights, focusing on how you tailored your message for the audience.

Example

“I once presented user engagement metrics to the marketing team. I used visual aids like graphs and charts to illustrate trends and avoided technical jargon. I focused on actionable insights, explaining how the data could inform their strategies, which helped them grasp the implications quickly.”

Product and Business Acumen

8. How do you prioritize which data projects to pursue?

This question evaluates your strategic thinking and understanding of business priorities.

How to Answer

Discuss how you align data projects with business goals, considering factors like potential impact, resource availability, and stakeholder input.

Example

“I prioritize data projects based on their alignment with business objectives and potential ROI. I consult with stakeholders to understand their needs and assess the feasibility of projects. For instance, I once prioritized a user retention analysis that directly supported a marketing campaign, leading to a significant increase in user engagement.”

9. What metrics would you define to measure the success of a new product feature?

This question tests your ability to think critically about product development and success measurement.

How to Answer

Identify key performance indicators (KPIs) relevant to the feature and explain how they relate to user experience and business goals.

Example

“For a new messaging feature, I would define metrics such as user adoption rate, frequency of use, and user satisfaction scores. These metrics would help assess not only how many users are engaging with the feature but also how it enhances their overall experience with the product.”

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

Embrace the Collaborative Spirit

Grammarly values collaboration and teamwork, as evidenced by the positive experiences shared by candidates. Approach your interviews with a mindset of partnership rather than competition. Engage with your interviewers by asking questions and sharing your thoughts on case studies or technical problems. This will not only showcase your analytical skills but also demonstrate your ability to work well with others, which is crucial in a company that emphasizes a collaborative culture.

Prepare for a Multi-Faceted Interview Process

The interview process at Grammarly is comprehensive, often involving multiple stages, including technical assessments, case studies, and behavioral interviews. Familiarize yourself with the structure of the interviews and prepare accordingly. For instance, practice SQL and machine learning problems, as these are common topics. Additionally, be ready to discuss your past experiences in detail, focusing on your successes, failures, and learnings. This will help you articulate your journey and how it aligns with Grammarly's mission.

Showcase Your Analytical Thinking

Grammarly seeks candidates who can break down complex problems and provide actionable insights. During your interviews, emphasize your analytical skills by discussing specific examples where you used data to drive decisions or improve processes. Be prepared to explain your thought process clearly and concisely, as effective communication is key in a role that involves cross-functional collaboration.

Understand the Company’s Values

Grammarly's EAGER values—ethical, adaptable, gritty, empathetic, and remarkable—are central to its culture. Reflect on how your personal values align with these principles and be ready to share examples that demonstrate your commitment to them. This alignment will resonate with your interviewers and show that you are a good cultural fit for the team.

Be Ready for Technical Challenges

Expect to face technical challenges, including take-home assignments and case studies that require deep analytical skills. While these tasks can be time-consuming, they are designed to assess your problem-solving abilities. Approach these challenges methodically, ensuring that you not only arrive at the correct answer but also provide a clear rationale for your approach. If you encounter open-ended questions, think critically about various solutions and be prepared to discuss the trade-offs of each.

Communicate Your Passion for the Role

Express your enthusiasm for the Data Scientist position and your desire to contribute to Grammarly's mission of improving communication. Share specific reasons why you want to work at Grammarly, such as its innovative use of AI or its commitment to user experience. This genuine interest can set you apart from other candidates and leave a lasting impression on your interviewers.

Follow Up Thoughtfully

After your interviews, consider sending a thoughtful follow-up message to your interviewers. Thank them for their time and reiterate your excitement about the opportunity. If you discussed specific topics during the interview, reference them in your message to reinforce your engagement and interest in the role.

By following these tips, you can navigate the interview process at Grammarly with confidence and demonstrate that you are not only a skilled Data Scientist but also a great fit for their team-oriented culture. Good luck!

The interview process for a Data Scientist role at Grammarly is designed to assess both technical skills and cultural fit within the team. It typically consists of several stages, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Recruiter Call

The process begins with a phone call from a recruiter. This initial conversation lasts about 30-45 minutes and serves as an opportunity for the recruiter to gauge your interest in the role and the company. During this call, you will discuss your background, relevant experiences, and motivations for wanting to work at Grammarly. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role.

2. Take-Home Technical Assessment

Following the recruiter call, candidates are often required to complete a take-home technical assessment. This task typically involves analyzing a dataset and answering specific questions related to data interpretation, statistical analysis, or machine learning. Candidates are usually given a week to complete this assignment, which can take several hours to finish. It is essential to approach this task thoughtfully, as it is a critical component of the evaluation process.

3. Virtual Onsite Interviews

Candidates who successfully complete the take-home assessment will move on to a virtual onsite interview, which consists of multiple rounds. This stage usually includes 4-6 one-on-one interviews with various team members, including data scientists, product managers, and possibly the hiring manager. Each interview lasts approximately 45 minutes to an hour and covers a mix of technical questions, case studies, and behavioral assessments. Expect to discuss your past experiences, problem-solving approaches, and how you would handle specific data challenges relevant to Grammarly's products.

4. Case Study and Technical Questions

During the virtual onsite, candidates will engage in discussions that may involve case studies and technical questions related to machine learning, SQL, and data analysis. Interviewers will assess your analytical thinking, ability to communicate complex ideas clearly, and how you approach problem-solving in a collaborative environment. Be prepared to demonstrate your understanding of metrics, experimentation, and the impact of data-driven decisions on product development.

5. Final Interview and Cultural Fit Assessment

The final stage of the interview process may include a discussion focused on cultural fit and alignment with Grammarly's values. This interview often involves questions about your professional journey, your approach to teamwork, and how you embody the company's EAGER values (ethical, adaptable, gritty, empathetic, and remarkable). This is an opportunity for you to showcase your interpersonal skills and how you would contribute to the team dynamic.

As you prepare for your interview, consider the types of questions that may arise during these stages, particularly those that assess your technical expertise and your ability to work collaboratively within a team.

1. How would you assess the validity of the AB test result with a .04 p-value?

Your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value. How would you assess the validity of this result?

2. How would you differentiate between scrapers and real people in a dataset of page views?

Given a dataset of page views where each row represents one page view, how would you differentiate between scrapers and real people?

3. Why did the treatment group with $10 rewards have a lower response rate than the control group?

You designed an experiment to measure the impact of financial rewards on users’ response rates. The treatment group with $10 rewards has a 30% response rate, while the control group without rewards has a 50% response rate. Can you explain what happened and how you could improve this experimental design?

4. How would you test the close friends feature on Instagram Stories while accounting for network effects?

You want to test the close friends feature on Instagram Stories. How would you create a control group and a test group to account for network effects?

5. Create a function to find the length of the largest palindrome that can be made from a string’s characters.

6. How would you explain the impact of financial rewards on users’ response rates and improve the experimental design?

You designed an experiment to measure the impact of financial rewards on users’ response rates. The treatment group with $10 rewards had a 30% response rate, while the control group without rewards had a 50% response rate. Explain the results and suggest improvements for the experimental design.

7. How would you explain linear regression to a child, a first-year college student, and a seasoned mathematician?

Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician, tailoring each explanation to their understanding level.

8. What happens when you run logistic regression on perfectly linearly separable data?

Given a dataset of perfectly linearly separable data, describe the outcome of running logistic regression.

9. How would you evaluate and validate a decision tree model for predicting loan repayment?

As a data scientist at a bank, you need to build a decision tree model to predict loan repayment. Explain how you would evaluate if a decision tree is the right model and assess its performance before and after deployment.

10. How would you justify using a neural network model to non-technical stakeholders?

If tasked with building a neural network model to solve a business problem, explain how you would justify the model’s complexity and explain its predictions to non-technical stakeholders.

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

Describe the process by which random forest generates its forest and explain why it might be preferred over other algorithms like logistic regression.

To help you succeed in your Grammarly data scientist interviews, consider these tips based on interview experiences:

1. Understand Grammarly’s Products and Market Position:

   • Familiarize yourself with Grammarly’s range of products and how they leverage AI to assist in writing. This understanding will help frame your answers in context to the company’s domain.

2. Focus on Data-Driven Insights:

   • Grammarly values the ability to derive actionable insights from data. Brush up on your knowledge of statistical analysis, A/B testing, and machine learning techniques as these are critical areas in their interview process.

3. Align with Grammarly’s Core Values:

   • Grammarly emphasizes a culture that is ethical, adaptable, gritty, empathetic, and remarkable (EAGER). Prepare to answer behavioral questions demonstrating how you embody these values in your professional life.

What is the average salary for a Data Scientist at Grammarly?

According to Glassdoor, data scientists at Grammarly earn between $162K to $241K per year, with an average of $196K per year.

What kind of technical skills and experience are required for the Data Scientist role?

Grammarly looks for candidates with strong analytical and critical thinking skills, proficiency in programming languages such as SQL, Python, R, or Scala, and practical experience in data analysis, statistics, and machine learning. A Master’s degree in a quantitative field and at least 3-5 years of relevant work experience are typically required. Experience in experiment design, and statistical analysis is highly valued.

What makes Grammarly’s work environment and culture unique?

Grammarly operates under a remote-first hybrid model, allowing team members the flexibility to work primarily remotely with in-person collaboration a few weeks every quarter. The company values creativity, collaboration, and innovation, reinforced through its EAGER (ethical, adaptable, gritty, empathetic, and remarkable) values. Grammarly also supports professional growth, offers a connected team culture, and has comprehensive benefits.

What does Grammarly offer in terms of career and personal growth?

Grammarly provides extensive support for professional development through training, coaching, and regular feedback. The company fosters a connected team environment with various employee resource groups and programs celebrating accomplishments and promoting connection. Grammarly offers a competitive compensation package, including excellent health care benefits, 401(k) matching, ample paid time off, and various stipends for home office, wellness, and even pet care.

If you’re ambitious, innovative, and eager to be a part of an industry leader in AI communication assistance, then the Data Scientist role at Grammarly might be your perfect fit. With a supportive team and a culture that encourages growth and learning, you’ll have the platform to become a key player in shaping the future of communication.

For more insights about Grammarly and their interview process, check out our Grammarly Interview Guide, where we cover potential interview questions and detailed guides for various roles such as software engineer and data analyst.

At Interview Query, we provide you with the tools and knowledge to excel in your interview journey. Unlock your potential and gather the strategic insights needed to ace your interview.

Good luck with your interview!