Freewheel Data Scientist Interview Questions + Guide in 2025

Freewheel is a technology company that provides advanced advertising solutions for video, TV, and other media platforms, enabling advertisers to optimize their campaigns and reach target audiences effectively.

As a Data Scientist at Freewheel, you will be tasked with leveraging large datasets to derive actionable insights and support decision-making processes. Key responsibilities include developing predictive models, analyzing data trends, and collaborating with cross-functional teams to enhance advertising strategies. A strong foundation in statistics, machine learning, and programming languages such as Python and SQL is essential. Ideal candidates should possess excellent problem-solving skills, the ability to communicate complex data concepts clearly, and a passion for innovation within the advertising technology space. Familiarity with big data technologies and experience in real-time data analysis will also set you apart in this role.

This guide aims to equip you with tailored insights and preparation strategies, enhancing your confidence and performance during your interview at Freewheel.

The interview process for a Data Scientist role at Freewheel is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and experience.

1. Initial Screening

The process begins with an initial phone screening, usually conducted by an HR representative. This conversation is focused on understanding your background, discussing your interest in the role, and gauging your fit within Freewheel's culture. Expect to answer questions about your previous experiences and how they relate to the position you are applying for.

2. Technical Assessment

Following the initial screening, candidates often complete a technical assessment, which may take the form of an online coding challenge. This assessment typically includes a set of programming problems that test your algorithmic thinking and coding skills. Candidates are usually given a limited time to complete the challenge, which may involve dynamic programming, data structures, and basic SQL queries.

3. Phone Interviews

After successfully passing the technical assessment, candidates typically participate in one or more phone interviews. These interviews may be conducted by the hiring manager or team members and often include a mix of technical and behavioral questions. Be prepared to discuss your past projects in detail, as well as to solve coding problems in real-time. Interviewers may also ask situational questions to assess your problem-solving abilities and how you handle challenges.

4. Onsite Interviews

The final stage of the interview process usually involves onsite interviews, which can consist of multiple rounds with various team members and leads. During these interviews, candidates may be asked to present a project or case study, followed by technical discussions and problem-solving exercises. The onsite portion is designed to evaluate both your technical expertise and your interpersonal skills, as you will be interacting with potential colleagues and managers.

5. Final Evaluation

In some cases, candidates may have a final round with senior leadership or a VP, where they will discuss their vision for the role and how they can contribute to the team. This stage is crucial for assessing alignment with the company's goals and culture.

As you prepare for your interview, it's essential to be ready for a variety of questions that may arise throughout the process.

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

Understand the Company Culture

Freewheel is known for its friendly and collaborative environment, but recent structural changes may have impacted the interview process. Familiarize yourself with the company's mission and values, and be prepared to discuss how your personal values align with theirs. This will not only help you connect with your interviewers but also demonstrate your genuine interest in the company.

Prepare for a Variety of Interview Formats

The interview process at Freewheel can include multiple formats such as phone screens, coding challenges, and onsite presentations. Be ready to adapt to different interview styles, from technical assessments to behavioral questions. Practice articulating your past projects and experiences clearly, as interviewers often want to hear about your specific contributions and the impact of your work.

Brush Up on Technical Skills

Expect to encounter a mix of technical questions, including coding challenges and system design discussions. Review key concepts in data structures, algorithms, and relevant programming languages like Python and SQL. Given the emphasis on practical application, be prepared to solve problems on the spot and explain your thought process clearly.

Be Ready for Behavioral Questions

Behavioral questions are a staple in Freewheel interviews. Prepare to discuss your past experiences, particularly challenges you've faced and how you've overcome them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your problem-solving skills and ability to work in a team.

Communicate Clearly and Confidently

During the interview, clarity and confidence in your communication are crucial. Make sure to articulate your thoughts and reasoning as you tackle technical questions. If you encounter a question that stumps you, it's okay to pause and think aloud. This shows your analytical approach and can help interviewers understand your problem-solving process.

Engage with Your Interviewers

Interviews at Freewheel can sometimes feel disorganized, so take the initiative to engage with your interviewers. Ask clarifying questions if needed and express your enthusiasm for the role and the company. This not only demonstrates your interest but also helps create a more interactive and enjoyable interview experience.

Follow Up Thoughtfully

After your interview, consider sending a follow-up email to thank your interviewers for their time and reiterate your interest in the position. This is a great opportunity to reflect on any specific topics discussed during the interview and to express how you can contribute to the team.

By preparing thoroughly and approaching the interview with confidence and enthusiasm, you can make a strong impression and increase your chances of success at Freewheel. Good luck!

Experience and Background

1. Describe a data science project you worked on and the impact it had on the business.

This question assesses your practical experience and ability to communicate the significance of your work.

How to Answer

Focus on a specific project, detailing your role, the methodologies used, and the outcomes. Highlight how your contributions led to measurable results.

Example

“In my last role, I led a project to optimize our customer segmentation model using clustering techniques. By implementing this model, we increased targeted marketing efficiency by 30%, which directly contributed to a 15% increase in sales over the next quarter.”

2. How do you approach problem-solving in data science?

This question evaluates your analytical thinking and methodology.

How to Answer

Discuss your systematic approach to problem-solving, including data collection, analysis, and validation steps. Mention any frameworks or tools you prefer.

Example

“I start by clearly defining the problem and gathering relevant data. I then analyze the data using statistical methods and machine learning algorithms, iterating on my approach based on initial findings. Finally, I validate my results through cross-validation techniques to ensure robustness.”

3. Can you explain a time you failed to deliver a project? What did you learn?

This question gauges your resilience and ability to learn from setbacks.

How to Answer

Be honest about a specific failure, focusing on what went wrong and how you adapted. Emphasize the lessons learned and how they improved your future work.

Example

“I once underestimated the time required for data cleaning in a project, which led to delays. I learned the importance of thorough project planning and now always allocate extra time for data preparation in my timelines.”

4. What techniques do you use for feature selection?

This question tests your technical knowledge and understanding of model performance.

How to Answer

Discuss various techniques you employ for feature selection, such as correlation analysis, recursive feature elimination, or using algorithms like LASSO.

Example

“I typically use a combination of correlation analysis to identify highly correlated features and recursive feature elimination to systematically remove less important features. This approach helps in improving model performance and reducing overfitting.”

Machine Learning

1. Explain the difference between supervised and unsupervised learning.

This question assesses your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples of each to illustrate your understanding.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

2. How do you handle imbalanced datasets?

This question evaluates your understanding of data preprocessing techniques.

How to Answer

Discuss methods such as resampling, using different evaluation metrics, or employing algorithms that are robust to class imbalance.

Example

“I often use techniques like SMOTE to oversample the minority class or undersample the majority class. Additionally, I focus on metrics like F1-score or AUC-ROC instead of accuracy to better evaluate model performance on imbalanced datasets.”

3. Describe a machine learning algorithm you are most comfortable with.

This question allows you to showcase your expertise in a specific area.

How to Answer

Choose an algorithm you are familiar with, explain how it works, and discuss its applications.

Example

“I am most comfortable with decision trees. They are intuitive and easy to interpret, making them great for classification tasks. I often use them for customer segmentation, as they provide clear insights into decision-making processes.”

4. What is overfitting, and how can you prevent it?

This question tests your understanding of model evaluation and improvement.

How to Answer

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

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor generalization. I prevent it by using techniques like cross-validation to ensure the model performs well on unseen data and applying regularization methods to penalize overly complex models.”

Statistics & Probability

1. How do you interpret a p-value?

This question assesses your understanding of statistical significance.

How to Answer

Explain the concept of p-values and their role in hypothesis testing.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

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

This question tests your grasp of fundamental statistical concepts.

How to Answer

Define the Central Limit Theorem and discuss its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of the sample means 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.”

3. Explain the difference between Type I and Type II errors.

This question evaluates your understanding of hypothesis testing.

How to Answer

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

Example

“A Type I error occurs when we incorrectly reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, a Type I error could mean concluding a drug is effective when it is not, whereas a Type II error would mean failing to recognize an effective drug.”

4. How do you assess the normality of a dataset?

This question tests your knowledge of data analysis techniques.

How to Answer

Discuss various methods for assessing normality, such as visual inspections or statistical tests.

Example

“I assess normality using visual methods like Q-Q plots and histograms, as well as statistical tests like the Shapiro-Wilk test. If the data is not normally distributed, I consider transformations or non-parametric methods for analysis.”