Tubi Data Scientist Interview Questions + Guide in 2025

Tubi is a leading global entertainment company and the most watched free streaming service in the U.S. and Canada, dedicated to making storytelling accessible to all.

The Data Scientist role at Tubi is pivotal for enhancing the company’s experimentation platform, which is instrumental in guiding data-driven decisions across the organization. As a Data Scientist, you will be responsible for planning and executing experimentation strategies, particularly A/B testing and other methodologies, to continuously improve user experience and product offerings. You will collaborate with cross-functional teams to analyze and interpret data, providing actionable insights that align with Tubi's strategic objectives. Key responsibilities include enhancing statistical rigor and transparency of experimental frameworks, researching advanced methodologies to streamline testing processes, and providing expert evaluations of experiment designs.

A great fit for this role would possess strong technical skills in programming and data manipulation using languages such as SQL and Python, along with a deep understanding of statistical methods and experimental design. Additionally, excellent communication skills are crucial for presenting complex findings to non-technical stakeholders. Experience in the video streaming industry is a plus, but a passionate and innovative mindset is essential.

This guide is designed to help you prepare effectively for your interview with Tubi by outlining the expectations and key areas of focus for the Data Scientist role, ensuring you can present yourself as a well-qualified candidate.

The interview process for a Data Scientist at Tubi is designed to assess both technical expertise and cultural fit within the organization. Candidates can expect a structured yet welcoming experience throughout the various stages of the interview.

1. Initial Recruiter Call

The process typically begins with a 30-minute phone call with a recruiter. This conversation serves as an introduction to Tubi and the role, allowing the recruiter to gauge your background, skills, and motivations. Expect to discuss your experience in data science, particularly in areas relevant to experimentation and A/B testing, as well as your interest in Tubi's mission and products.

2. Manager Interview

Following the initial call, candidates will have a one-on-one interview with the hiring manager. This session focuses on your technical capabilities and how they align with Tubi's goals. You may be asked to elaborate on your previous projects, particularly those involving statistical analysis and experimentation. The manager will also assess your problem-solving approach and how you collaborate with cross-functional teams.

3. Technical Phone Screen

Next, candidates will undergo a technical phone screen, which may involve a live coding exercise or a discussion of technical concepts. This interview is typically conducted by a senior data scientist and will cover topics such as experimental design, statistical methods, and programming skills in SQL, Python, or Spark. Be prepared to solve problems on the spot and explain your thought process clearly.

4. Take-Home Assessment

In some cases, candidates may be required to complete a take-home assessment. This task usually involves analyzing a dataset and providing insights or recommendations based on your findings. The goal is to evaluate your analytical skills and ability to communicate complex results effectively.

5. Onsite Interview

The final stage is an onsite interview, which consists of multiple rounds with various team members, including data scientists and business partners. Each round will focus on different aspects of the role, such as advanced statistical techniques, product metrics, and collaboration strategies. Expect a mix of technical questions, case studies, and behavioral assessments to determine how well you fit within Tubi's culture.

Throughout the process, candidates are encouraged to ask questions and engage with interviewers to demonstrate their enthusiasm for the role and the company.

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

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

Emphasize Your Passion for Data-Driven Decision Making

Tubi values a culture of innovation and data-driven experimentation. During your interview, express your enthusiasm for using data to inform decisions and improve user experiences. Share specific examples from your past experiences where your data analysis led to actionable insights or significant improvements. This will demonstrate your alignment with Tubi's mission and your commitment to fostering a culture of experimentation.

Prepare for Technical Questions with Real-World Applications

Expect to encounter technical questions focused on A/B testing, experimental design, and statistical methods. Be prepared to discuss your experience with these concepts in detail. Use real-world examples to illustrate your understanding of how to design experiments, analyze results, and apply findings to drive business outcomes. Familiarize yourself with Tubi's specific metrics and frameworks, as this will show your dedication to understanding their unique challenges.

Showcase Your Collaboration Skills

Tubi's data science team collaborates closely with cross-functional teams. Highlight your experience working in collaborative environments and your ability to communicate complex findings to non-technical stakeholders. Prepare to discuss how you have successfully partnered with other departments to achieve common goals, as this will demonstrate your fit within Tubi's team-oriented culture.

Understand the Streaming Industry Landscape

Having a grasp of the video streaming industry, including trends and challenges, will set you apart. Research Tubi's competitors and the unique value proposition Tubi offers to its users. Be ready to discuss how your skills can contribute to Tubi's growth in this competitive landscape, particularly in areas like user engagement and retention.

Be Ready for a Smooth Interview Process

Candidates have reported a smooth and welcoming interview experience at Tubi. Approach each stage of the interview process with confidence and positivity. Engage with your interviewers by asking insightful questions about their experiences at Tubi and the company's future direction. This will not only help you gauge if Tubi is the right fit for you but also leave a lasting impression on your interviewers.

Follow Up Thoughtfully

After your interview, send a personalized thank-you note to your interviewers. Mention specific topics discussed during the interview to reinforce your interest and engagement. This small gesture can help you stand out and demonstrate your professionalism and enthusiasm for the role.

By following these tips, you can position yourself as a strong candidate for the Data Scientist role at Tubi. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Tubi. The interview process will likely focus on your technical expertise in data science, particularly in A/B testing and experimentation, as well as your ability to communicate complex findings to non-technical stakeholders. Be prepared to demonstrate your analytical skills and your understanding of statistical methods and experimental design.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial for this role, as it will help you articulate your approach to data analysis.

How to Answer

Discuss the key differences, including how supervised learning uses labeled data while unsupervised learning deals with unlabeled data. Provide examples of each type.

Example

“Supervised learning involves training a model on a labeled dataset, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning analyzes data without labeled outcomes, such as clustering customers based on purchasing behavior.”

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

This question assesses your practical experience and problem-solving skills in real-world scenarios.

How to Answer

Highlight a specific project, the challenges encountered, and how you overcame them. Focus on the impact of your work.

Example

“I worked on a recommendation system for an e-commerce platform. One challenge was dealing with sparse data, which I addressed by implementing collaborative filtering techniques. This improved the accuracy of our recommendations, leading to a 15% increase in user engagement.”

3. How do you handle overfitting in a model?

This question tests your understanding of model evaluation and improvement techniques.

How to Answer

Discuss various strategies to prevent overfitting, such as cross-validation, regularization, and pruning.

Example

“To handle overfitting, I often use cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization techniques like Lasso or Ridge regression to penalize overly complex models, which helps maintain a balance between bias and variance.”

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

Understanding model evaluation is key to ensuring the effectiveness of your data-driven decisions.

How to Answer

Mention common metrics relevant to the type of model you are discussing, such as accuracy, precision, recall, F1 score, and AUC-ROC.

Example

“I typically use accuracy for classification tasks, but I also consider precision and recall to understand the trade-offs better. For imbalanced datasets, I prefer the F1 score, as it provides a balance between precision and recall.”

Statistics & Probability

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

This question assesses your grasp of statistical significance and hypothesis testing.

How to Answer

Define p-value and explain its role in determining the strength of evidence against the null hypothesis.

Example

“A p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

2. How do you determine sample size for an experiment?

This question evaluates your understanding of experimental design and statistical power.

How to Answer

Discuss the factors that influence sample size, including effect size, desired power, and significance level.

Example

“To determine sample size, I consider the expected effect size, the desired power of the test (commonly 0.8), and the significance level (usually 0.05). I use power analysis to calculate the minimum sample size needed to detect a meaningful effect.”

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

This question tests your foundational knowledge of statistics.

How to Answer

Explain the theorem and its implications for sampling distributions.

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.”

4. Can you describe A/B testing and its importance?

A/B testing is a key component of experimentation, and understanding it is vital for this role.

How to Answer

Define A/B testing and explain its purpose in making data-driven decisions.

Example

“A/B testing involves comparing two versions of a variable to determine which one performs better. It’s essential for optimizing user experience and making informed decisions based on empirical data rather than assumptions.”

Data Manipulation & Analysis

1. What tools and programming languages are you proficient in for data analysis?

This question assesses your technical skills and familiarity with relevant tools.

How to Answer

List the programming languages and tools you are experienced with, emphasizing their relevance to the role.

Example

“I am proficient in Python and SQL for data manipulation and analysis. I also have experience with Spark for handling large datasets and libraries like Pandas and NumPy for data analysis.”

2. Describe a time when you had to clean and preprocess a dataset. What steps did you take?

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

How to Answer

Outline the specific steps you took to clean and preprocess the data, including handling missing values and outliers.

Example

“In a recent project, I worked with a dataset that had numerous missing values. I first assessed the extent of the missing data and decided to impute values for some features while removing others. I also standardized the data to ensure consistency across different scales.”

3. How do you ensure the accuracy and integrity of your data?

This question tests your approach to data quality.

How to Answer

Discuss the methods you use to validate and verify data accuracy.

Example

“I ensure data accuracy by implementing validation checks during data collection and preprocessing. I also perform exploratory data analysis to identify anomalies and inconsistencies, which I address before proceeding with analysis.”

4. Can you explain how you would approach a data analysis project from start to finish?

This question assesses your project management and analytical skills.

How to Answer

Outline the steps you would take, from defining the problem to presenting findings.

Example

“I start by clearly defining the problem and objectives. Next, I gather and preprocess the data, followed by exploratory data analysis to uncover insights. I then apply appropriate statistical methods or machine learning models, interpret the results, and finally present my findings to stakeholders in a clear and actionable manner.”