Leidos Data Scientist Interview Questions + Guide in 2025

Leidos is a global leader in providing technology solutions for government and commercial sectors, focusing on national security, health, and engineering initiatives.

The Data Scientist role at Leidos is essential for transforming complex data into actionable insights that drive strategic decision-making. Key responsibilities include collecting, processing, and analyzing large datasets, utilizing machine learning and statistical modeling techniques to solve critical problems. Candidates must be proficient in programming languages such as Python and SQL, possess experience with data visualization tools like Tableau, and have a strong understanding of data architecture and cloud technologies. Additionally, an ideal candidate will demonstrate exceptional communication skills, capable of conveying complex quantitative analyses in an accessible manner to both technical and non-technical stakeholders.

Success in this role aligns with Leidos' commitment to innovation and customer success, and candidates should be prepared to showcase their problem-solving capabilities and collaborative spirit. This guide will help you navigate the interview process by highlighting the key skills and knowledge areas that Leidos values, ultimately increasing your chances of making a strong impression.

The interview process for a Data Scientist position at Leidos is structured to assess both technical expertise and cultural fit within the organization. Candidates can expect a multi-step process that includes several rounds of interviews, each designed to evaluate different aspects of their qualifications and experience.

1. Initial Screening

The process typically begins with an initial screening conducted by a recruiter. This is usually a brief phone interview lasting around 15-30 minutes. During this call, the recruiter will discuss the role, the company culture, and gather information about your background, including your experience in data science and relevant technical skills. This is also an opportunity for candidates to ask questions about the position and the team.

2. Technical Interview

Following the initial screening, candidates may be invited to a technical interview, which can be conducted via phone or video conferencing. This interview is often led by a current Data Scientist at Leidos and focuses on assessing your technical knowledge and problem-solving abilities. Expect questions related to machine learning, data processing, and programming languages such as Python and SQL. You may also be asked to solve coding problems or discuss your previous projects in detail.

3. Behavioral Interview

After the technical interview, candidates typically participate in a behavioral interview. This round is designed to evaluate how well you align with Leidos' values and culture. Interviewers will ask about your past experiences, teamwork, and how you handle challenges. Be prepared to provide specific examples that demonstrate your problem-solving skills, adaptability, and ability to work collaboratively in a team environment.

4. Onsite Interview (or Final Round)

The final stage of the interview process may involve an onsite interview, which can include multiple rounds with different team members, including potential supervisors and senior management. This stage often includes a mix of technical assessments, case studies, and discussions about your approach to data analysis and project management. Candidates may also be asked to present a portfolio of their work or complete a practical exercise related to data science.

5. Follow-Up and Offer

After the onsite interviews, candidates will typically receive feedback within a few weeks. If selected, you will receive a formal job offer, which may include discussions about salary, benefits, and other employment terms.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during each stage of the process. Here are some examples of the interview questions that candidates have encountered in the past.

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

Understand the Role and Its Impact

Before your interview, take the time to deeply understand the specific responsibilities of a Data Scientist at Leidos. Familiarize yourself with how data analysis contributes to national security and mission-critical operations. Be prepared to discuss how your skills in data collection, processing, and analysis can directly support the company's objectives, particularly in areas like maritime features and intelligence gathering.

Prepare for Technical Questions

Given the technical nature of the role, you should be ready to answer questions related to machine learning, data manipulation, and programming languages such as Python and SQL. Review key concepts like Bayesian vs. Markov models, unsupervised learning, and transfer learning, as these topics have been highlighted in past interviews. Practicing coding problems and data analysis scenarios will also help you demonstrate your technical proficiency.

Showcase Your Communication Skills

Leidos values the ability to communicate complex quantitative analyses clearly and effectively. Prepare to discuss how you would present your findings to both technical and non-technical audiences. Use storytelling techniques to explain your data-driven insights, and be ready to provide examples of how you've successfully communicated results in previous roles.

Emphasize Collaboration and Teamwork

The interview process may involve multiple rounds with various team members, including potential supervisors. Highlight your experience working in collaborative environments and your ability to contribute to team success. Be prepared to discuss how you’ve worked with cross-functional teams in the past, especially in high-stakes or fast-paced settings.

Familiarize Yourself with Company Culture

Leidos emphasizes a commitment to doing the right thing for customers, employees, and the community. Reflect on how your personal values align with this mission. Be ready to discuss how you can contribute to a positive work environment and support the company’s goals of innovation and community engagement.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Prepare examples that demonstrate your analytical thinking, creativity in solving challenges, and how you handle setbacks or changes in project scope.

Follow Up with Questions

At the end of your interview, have thoughtful questions prepared that show your interest in the role and the company. Inquire about the team dynamics, ongoing projects, or how success is measured in the Data Scientist role. This not only demonstrates your enthusiasm but also helps you gauge if Leidos is the right fit for you.

By following these tips, you can present yourself as a well-prepared and enthusiastic candidate who is ready to contribute to Leidos' mission. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Leidos. The interview process will likely focus on your technical skills, experience with data analysis, machine learning, and your ability to communicate complex concepts clearly. Be prepared to discuss your past projects and how they relate to the responsibilities outlined in the job description.

Machine Learning

1. What is the difference between Bayesian and Markov models?

Understanding the distinctions between these models is crucial for data scientists, especially in probabilistic modeling.

How to Answer

Explain the fundamental principles of both models, emphasizing their applications and differences in assumptions about data.

Example

“Bayesian models incorporate prior knowledge and update beliefs based on new evidence, while Markov models rely on the principle that future states depend only on the current state, not on the sequence of events that preceded it. This makes Bayesian models more flexible in scenarios with limited data.”

2. Can you explain transfer learning and its applications?

Transfer learning is a key concept in machine learning, especially in deep learning.

How to Answer

Discuss how transfer learning allows models trained on one task to be adapted for another, saving time and resources.

Example

“Transfer learning involves taking a pre-trained model on a large dataset and fine-tuning it on a smaller, task-specific dataset. This is particularly useful in image recognition tasks where labeled data is scarce, allowing us to leverage the knowledge gained from a broader dataset.”

3. Describe your experience with unsupervised learning techniques.

Unsupervised learning is essential for data exploration and pattern recognition.

How to Answer

Share specific techniques you’ve used, such as clustering or dimensionality reduction, and the outcomes of those projects.

Example

“I have utilized K-means clustering to segment customer data based on purchasing behavior, which helped identify distinct customer profiles and tailor marketing strategies accordingly. Additionally, I’ve applied PCA for feature reduction in high-dimensional datasets, improving model performance.”

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

Model evaluation is critical to ensure the effectiveness of your solutions.

How to Answer

Discuss various metrics and methods you use to assess model performance, including confusion matrices, ROC curves, and cross-validation.

Example

“I typically use accuracy, precision, recall, and F1-score for classification models, along with ROC-AUC for a comprehensive evaluation. For regression tasks, I rely on metrics like RMSE and R-squared. I also implement cross-validation to ensure the model generalizes well to unseen data.”

5. What are some common challenges you face when implementing machine learning models?

Understanding challenges shows your practical experience in the field.

How to Answer

Mention specific challenges such as data quality, overfitting, and model interpretability, along with strategies to overcome them.

Example

“One common challenge is dealing with imbalanced datasets, which can lead to biased models. I address this by using techniques like SMOTE for oversampling the minority class or adjusting class weights during model training to ensure a more balanced approach.”

Statistics & Probability

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

P-values are fundamental in statistical analysis and hypothesis testing.

How to Answer

Define p-values and explain their role in determining statistical significance.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A common threshold is 0.05, where a p-value below this suggests strong evidence against the null hypothesis, leading to its rejection.”

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

The Central Limit Theorem is a cornerstone of statistics.

How to Answer

Discuss the theorem's implications for sampling distributions and inferential statistics.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters based on sample statistics.”

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

Handling missing data is a common task in data preprocessing.

How to Answer

Describe various techniques for dealing with missing data, such as imputation or deletion.

Example

“I typically assess the extent and pattern of missing data first. For small amounts, I might use mean or median imputation, while for larger gaps, I prefer more sophisticated methods like KNN imputation or predictive modeling to estimate missing values.”

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

Understanding errors in hypothesis testing is essential for data scientists.

How to Answer

Define both types of errors and their implications in decision-making.

Example

“A Type I error occurs when we reject a true null hypothesis, leading to a false positive, while a Type II error happens when we fail to reject a false null hypothesis, resulting in a false negative. Balancing these errors is crucial in hypothesis testing to minimize incorrect conclusions.”

5. What is the purpose of A/B testing?

A/B testing is a practical application of statistics in decision-making.

How to Answer

Explain the methodology and its significance in evaluating changes or interventions.

Example

“A/B testing involves comparing two versions of a variable to determine which performs better. By randomly assigning subjects to each group and analyzing the results, we can make data-driven decisions about product features or marketing strategies.”

Data Manipulation & Analysis

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

SQL is a fundamental skill for data scientists.

How to Answer

Discuss your proficiency with SQL and specific tasks you’ve accomplished using it.

Example

“I have extensive experience writing complex SQL queries to extract, manipulate, and analyze data from relational databases. For instance, I used SQL to join multiple tables and perform aggregations to generate reports on customer behavior, which informed our marketing strategies.”

2. How do you approach data cleaning and preprocessing?

Data cleaning is a critical step in the data analysis process.

How to Answer

Outline your systematic approach to cleaning and preparing data for analysis.

Example

“I start by assessing the dataset for missing values, duplicates, and inconsistencies. I then apply techniques such as normalization, encoding categorical variables, and outlier detection to ensure the data is clean and ready for analysis. This step is crucial for accurate modeling.”

3. What tools do you use for data visualization, and why?

Data visualization is key to communicating insights effectively.

How to Answer

Mention specific tools you are proficient in and their advantages.

Example

“I primarily use Tableau and Matplotlib for data visualization. Tableau allows for interactive dashboards that are user-friendly for stakeholders, while Matplotlib provides flexibility for custom visualizations in Python. Both tools help convey complex data insights clearly.”

4. Can you explain the process of feature engineering?

Feature engineering is vital for improving model performance.

How to Answer

Discuss the importance of creating new features and techniques you use.

Example

“Feature engineering involves creating new variables from existing data to improve model performance. I often use techniques like polynomial features for regression tasks or encoding categorical variables using one-hot encoding to capture relationships that enhance predictive power.”

5. How do you ensure the quality and integrity of your data?

Data quality is essential for reliable analysis.

How to Answer

Describe your methods for maintaining data quality throughout the analysis process.

Example

“I implement data validation checks at various stages of the data pipeline, including verifying data types, ranges, and consistency. Additionally, I use automated scripts to monitor data quality metrics and flag any anomalies for further investigation.”