Ibotta, Inc. Data Scientist Interview Questions + Guide in 2025

Ibotta, Inc. is a leading performance marketing platform that enables brands to connect with over 200 million consumers through a network of digital promotions.

As a Data Scientist at Ibotta, you will play a crucial role in the Core Data & Analytics team, contributing to the mission of making every purchase rewarding. Your primary responsibilities will include leveraging cutting-edge statistics, machine learning techniques, and data visualization methods to extract actionable insights that inform business decisions and drive growth. You will collaborate with cross-functional teams, including stakeholders, data engineers, and architects, to discover, define, cleanse, and refine data essential for analysis and modeling.

To excel in this role, you should possess strong analytical skills and a solid foundation in statistical and machine learning algorithms, along with proficiency in programming languages such as Python or R. An understanding of MLOps and experience manipulating complex data within data lakes and distributed systems will be advantageous. Furthermore, your work will be grounded in Ibotta's core values of Integrity, Boldness, Ownership, Teamwork, Transparency, and the belief that a good idea can come from anywhere.

This guide will equip you with the knowledge and insights needed to prepare effectively for your interview, ensuring you can articulate how your skills and experiences align with Ibotta's goals and values.

The interview process for a Data Scientist role at Ibotta is structured to assess both technical and interpersonal skills, ensuring candidates align with the company's mission and values. The process typically includes several key stages:

1. Initial Recruiter Call

The first step involves a 30 to 60-minute phone interview with a recruiter. This conversation serves as an introduction to the role and the company, where the recruiter will discuss your background, motivations for applying, and assess your fit for Ibotta's culture. Expect to share your experiences and how they relate to the responsibilities of a Data Scientist.

2. Technical Interview

Following the recruiter call, candidates usually participate in a technical interview, which may be conducted via video conferencing. This session typically lasts about an hour and focuses on your technical expertise in data science, including statistical analysis, machine learning algorithms, and programming skills in languages such as Python or R. You may be asked to solve problems on the spot or discuss past projects in detail.

3. Take-Home Project

Candidates who perform well in the technical interview are often given a take-home project. This project is designed to evaluate your ability to apply data science techniques to real-world problems. You will typically have a few days to complete the project, which may involve data analysis, model building, and presenting your findings. Be prepared to explain your thought process and the methodologies you used.

4. Final Interview Rounds

The final stage usually consists of one or more interviews with team members, including the hiring manager and other data scientists. These interviews delve deeper into your technical skills, problem-solving abilities, and how you collaborate with others. Expect to discuss your approach to data-driven decision-making, stakeholder engagement, and how you would contribute to the team’s goals.

5. Cultural Fit Assessment

Throughout the interview process, Ibotta places a strong emphasis on cultural fit. You may be asked questions that assess your alignment with Ibotta's core values, such as integrity, teamwork, and ownership. This is to ensure that you not only have the technical skills but also the right mindset to thrive in their collaborative environment.

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

Ibotta values integrity, boldness, ownership, teamwork, and transparency. Familiarize yourself with these core values and think of examples from your past experiences that demonstrate how you embody these principles. During the interview, weave these values into your responses to show that you align with the company culture.

Prepare for Technical Assessments

Given the emphasis on statistical analysis, machine learning, and data visualization, ensure you are well-versed in relevant tools and languages such as Python, R, SQL, and Spark. Be ready to discuss your experience with various machine learning algorithms and statistical techniques. Practice coding problems and data manipulation tasks that may be part of the technical assessment.

Showcase Your Problem-Solving Skills

Ibotta seeks candidates who can analyze large datasets to extract actionable insights. Prepare to discuss specific projects where you identified a problem, analyzed data, and implemented a solution. Use the STAR (Situation, Task, Action, Result) method to structure your responses, highlighting your analytical and problem-solving skills.

Emphasize Collaboration and Communication

As a Data Scientist at Ibotta, you will work closely with stakeholders, data engineers, and other teams. Be prepared to discuss how you have successfully collaborated on cross-functional projects in the past. Highlight your ability to communicate complex data insights in a clear and actionable manner, as this is crucial for driving business decisions.

Be Ready for Behavioral Questions

Expect questions that assess your fit within the team and company culture. Reflect on your past experiences and prepare to discuss challenges you've faced, how you overcame them, and what you learned. This will demonstrate your growth mindset and ability to adapt, which are highly valued at Ibotta.

Prepare for the Take-Home Project

If you are given a take-home project, approach it with diligence and creativity. Ensure you understand the requirements clearly and allocate sufficient time to complete it. Document your thought process and methodologies, as this will help you articulate your approach during follow-up discussions. If you receive feedback, be open to it and use it as a learning opportunity.

Stay Informed About Industry Trends

Ibotta is at the forefront of performance marketing and data analytics. Stay updated on the latest trends in data science, machine learning, and marketing analytics. Being knowledgeable about industry advancements will not only help you in the interview but also demonstrate your passion for continuous learning and development.

Follow Up Thoughtfully

After the interview, send a thank-you email to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your enthusiasm for the role and the company. If you discussed specific topics during the interview, reference them in your follow-up to reinforce your interest and engagement.

By preparing thoroughly and aligning your experiences with Ibotta's values and expectations, you will position yourself as a strong candidate for the Data Scientist role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Ibotta. Candidates should focus on demonstrating their technical expertise in data science, machine learning, and statistical analysis, as well as their ability to collaborate with cross-functional teams and drive business insights.

Machine Learning

1. How do you set up a data science model if you have no pre-existing data from your process you are trying to model?

This question assesses your understanding of model development in the absence of historical data.

How to Answer

Discuss the importance of defining the problem, identifying potential data sources, and considering synthetic data generation or simulations to create a baseline for your model.

Example

"I would start by clearly defining the problem and the objectives of the model. Then, I would explore potential data sources, such as external datasets or industry benchmarks. If necessary, I could also create synthetic data through simulations to establish a baseline for model training and validation."

2. Can you explain the difference between supervised and unsupervised learning?

This question tests your foundational knowledge of machine learning techniques.

How to Answer

Clearly differentiate between the two types of learning, providing examples of each and their typical use cases.

Example

"Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like customer segmentation based on purchasing behavior."

3. Describe a machine learning project you have worked on. What challenges did you face?

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them, emphasizing your analytical and technical skills.

Example

"I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced classes, as most customers did not churn. I addressed this by using techniques like SMOTE for oversampling and adjusting the classification threshold to improve model performance."

4. What techniques do you use to evaluate the performance of a machine learning model?

This question assesses your understanding of model evaluation metrics.

How to Answer

Discuss various metrics relevant to the type of model you are evaluating, such as accuracy, precision, recall, F1 score, and ROC-AUC for classification models.

Example

"I typically use accuracy, precision, recall, and F1 score for classification models, as they provide a comprehensive view of performance. For regression models, I prefer metrics like RMSE and R-squared to assess how well the model predicts continuous outcomes."

Statistics & Probability

1. Explain the concept of p-value in hypothesis testing.

This question tests your understanding of statistical significance.

How to Answer

Define p-value and its role in hypothesis testing, including its implications for rejecting or failing to reject the null hypothesis.

Example

"The p-value measures the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) indicates strong evidence against the null hypothesis, leading us to reject it in favor of the alternative hypothesis."

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

This question evaluates your data preprocessing skills.

How to Answer

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

Example

"I handle missing data by first assessing the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques like mean or median substitution, or I could opt to delete rows or columns with excessive missing values. In some cases, I may also use models that can handle missing data directly."

3. What is the Central Limit Theorem and why is it important?

This question assesses your understanding of fundamental statistical concepts.

How to Answer

Explain the Central Limit Theorem and its significance in inferential statistics.

Example

"The Central Limit Theorem states that the distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial because it allows us to make inferences about population parameters using sample statistics, enabling hypothesis testing and confidence interval estimation."

4. Can you explain the difference between Type I and Type II errors?

This question tests your knowledge of hypothesis testing errors.

How to Answer

Define both types of errors and provide examples to illustrate the differences.

Example

"A Type I error occurs when we reject a true null hypothesis, essentially a false positive. For example, concluding that a new drug is effective when it is not. A Type II error happens when we fail to reject a false null hypothesis, or a false negative, such as concluding that a drug is ineffective when it actually is."

Data Manipulation & Analysis

1. Describe your experience with SQL and how you use it in data analysis.

This question assesses your technical skills in database management.

How to Answer

Discuss your proficiency in SQL, including specific functions or queries you frequently use for data extraction and analysis.

Example

"I have extensive experience with SQL, using it to extract and manipulate data from relational databases. I often use JOINs to combine tables, GROUP BY for aggregating data, and window functions for advanced analytics, such as calculating running totals or moving averages."

2. How do you approach feature engineering in a dataset?

This question evaluates your ability to enhance model performance through data preparation.

How to Answer

Discuss your strategies for creating new features from existing data and the importance of domain knowledge in this process.

Example

"I approach feature engineering by first understanding the domain and the problem at hand. I analyze existing features for correlations with the target variable and create new features based on domain knowledge, such as aggregating transaction data to derive customer lifetime value or creating interaction terms to capture relationships between variables."

3. What tools or libraries do you use for data visualization?

This question assesses your ability to communicate insights effectively.

How to Answer

Mention specific tools or libraries you are familiar with and how you use them to visualize data.

Example

"I frequently use libraries like Matplotlib and Seaborn in Python for creating static visualizations, and Plotly for interactive dashboards. I also utilize Tableau for more complex visualizations and to share insights with stakeholders in a user-friendly format."

4. How do you ensure data quality and integrity in your analyses?

This question evaluates your attention to detail and data governance practices.

How to Answer

Discuss your methods for validating data quality, including checks for accuracy, completeness, and consistency.

Example

"I ensure data quality by implementing validation checks at various stages of the data pipeline. This includes verifying data types, checking for duplicates, and conducting exploratory data analysis to identify outliers or inconsistencies. I also document data sources and transformations to maintain transparency and reproducibility."