Viasat Inc. Data Scientist Interview Questions + Guide in 2025

Viasat Inc. is on a mission to deliver connections with the capacity to change the world, providing innovative communication solutions for consumers, businesses, and governments alike.

As a Data Scientist at Viasat, you will play a pivotal role in developing advanced data-driven solutions that enhance product performance and customer engagement. Your key responsibilities will include collaborating with multi-functional teams to build and refine a satellite coverage evaluator service, leveraging sophisticated data science and machine learning techniques to analyze large datasets. Proficiency in Python and SQL is essential, along with a strong foundation in mathematics, algorithms, and data manipulation skills. You’ll be expected to effectively handle both structured and unstructured data, ensuring its integrity and usability for production-ready applications.

The ideal candidate embodies Viasat's values by thinking big, acting fearlessly, and fostering an inclusive environment. Strong communication skills and a collaborative spirit are crucial, as you will be working closely with diverse teams across various time zones. This guide will help you prepare for your interview by equipping you with insights into the role's expectations and the skills that will set you apart as a candidate.

The interview process for a Data Scientist role at Viasat Inc. is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages designed to evaluate your expertise in data science methodologies, problem-solving abilities, and collaborative skills.

1. Initial Phone Screen

The first step in the interview process is an initial phone screen, which usually lasts about 30-45 minutes. This interview is typically conducted by a recruiter or HR representative. During this call, you will discuss your background, experiences, and motivations for applying to Viasat. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role. This is an opportunity for you to showcase your communication skills and express your enthusiasm for the position.

2. Technical Phone Interview

Following the initial screen, candidates typically participate in a technical phone interview. This round is conducted by a member of the technical team and focuses on your technical expertise, particularly in Python and SQL, as well as your understanding of data manipulation and analysis. Expect to engage in discussions about your previous projects, coding challenges, and possibly some case study scenarios that require you to demonstrate your analytical thinking and problem-solving skills.

3. Onsite Interview

The final stage of the interview process is an onsite interview, which consists of multiple rounds—usually around five interviews with different team members. These interviews will cover a range of topics, including technical questions related to data science, algorithms, and product metrics. You may also be presented with case studies that require you to apply a data-driven approach to real-world problems, such as customer segmentation or performance evaluation of satellite services. Each interview is designed to assess not only your technical capabilities but also your ability to collaborate with cross-functional teams and communicate your findings effectively.

Throughout the interview process, candidates are encouraged to ask questions and engage with their interviewers, as Viasat values a collaborative and inclusive environment.

Now that you have an understanding of the interview process, let’s delve into the specific questions that candidates have encountered during their interviews.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Viasat Inc. The interview process will likely focus on your technical skills, problem-solving abilities, and collaborative mindset, as well as your understanding of data science principles and methodologies. Be prepared to discuss your experience with data manipulation, machine learning, and your approach to working with large datasets.

Technical Skills

1. Can you explain the process you follow for cleaning and preparing data for analysis?

This question assesses your data wrangling skills and your understanding of the importance of data quality.

How to Answer

Discuss the steps you take to clean data, including identifying inconsistencies, handling missing values, and transforming data types. Emphasize the tools and techniques you use to ensure the data is ready for analysis.

Example

“I typically start by exploring the dataset to identify any missing or inconsistent values. I use Python libraries like Pandas to handle missing data through imputation or removal, depending on the context. I also standardize data formats and types to ensure consistency, which is crucial for accurate analysis.”

2. Describe a machine learning project you have worked on. What was your role, and what were the outcomes?

This question evaluates your practical experience with machine learning.

How to Answer

Highlight a specific project, your contributions, the algorithms used, and the results achieved. Focus on the impact of the project and any lessons learned.

Example

“In my last project, I developed a predictive model to forecast customer engagement based on historical data. I was responsible for feature engineering and model selection, ultimately using a random forest algorithm. The model improved our engagement predictions by 20%, allowing the marketing team to tailor their strategies effectively.”

3. How do you approach feature selection for a machine learning model?

This question tests your understanding of feature engineering and its importance in model performance.

How to Answer

Discuss the methods you use for feature selection, such as correlation analysis, recursive feature elimination, or using domain knowledge. Explain how you assess the impact of features on model performance.

Example

“I start by analyzing the correlation between features and the target variable to identify potentially useful features. I also use techniques like recursive feature elimination to iteratively remove less important features. This helps in reducing overfitting and improving model interpretability.”

4. What is your experience with SQL, and how do you use it in your data analysis?

This question gauges your proficiency with SQL and its application in data manipulation.

How to Answer

Explain your experience with SQL, including the types of queries you write and how you use SQL to extract and manipulate data for analysis.

Example

“I have extensive experience using SQL for data extraction and manipulation. I often write complex queries involving joins and aggregations to gather insights from large datasets. For instance, I recently used SQL to analyze customer behavior patterns by joining multiple tables to create a comprehensive view of user interactions.”

5. Can you discuss a time when you had to collaborate with a multi-functional team? What challenges did you face?

This question assesses your teamwork and communication skills.

How to Answer

Share a specific example of a project where you collaborated with different teams, the challenges encountered, and how you overcame them.

Example

“During a project to optimize our data pipeline, I worked closely with the engineering and product teams. One challenge was aligning our goals and timelines. I facilitated regular meetings to ensure everyone was on the same page, which helped us successfully launch the optimized pipeline ahead of schedule.”

Statistics and Probability

1. Explain the difference between supervised and unsupervised learning.

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Define both terms clearly and provide examples of each type of learning.

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, where the model identifies patterns or groupings, like clustering customers based on purchasing behavior.”

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

This question assesses your understanding of model evaluation metrics.

How to Answer

Discuss the metrics you use to evaluate model performance, such as accuracy, precision, recall, and F1 score, and explain when to use each.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, but I prefer precision and recall for imbalanced datasets. For instance, in a fraud detection model, I focus on recall to ensure we catch as many fraudulent cases as possible, even if it means sacrificing some precision.”

3. What is overfitting, and how can it be prevented?

This question tests your understanding of model training and validation.

How to Answer

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

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data. To prevent this, I use techniques like cross-validation to ensure the model generalizes well and apply regularization methods to penalize overly complex models.”

4. Can you explain the concept of p-values in hypothesis testing?

This question evaluates your knowledge of statistical significance.

How to Answer

Define p-values and explain their role in hypothesis testing, including what they indicate about the null hypothesis.

Example

“A 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) suggests that we can reject the null hypothesis, indicating that the observed effect is statistically significant.”

5. How would you handle missing data in a dataset?

This question assesses your approach to data quality issues.

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. If it’s minimal, I might use mean or median imputation. For larger gaps, I consider using algorithms that can handle missing values directly or explore the possibility of creating a separate category for missing data.”