Kiewit Data Scientist Interview Questions + Guide in 2025

Kiewit is a leading construction and engineering firm that operates across multiple markets, delivering innovative and efficient solutions to complex challenges.

As a Data Scientist at Kiewit, you will play a pivotal role in harnessing the power of data to drive decision-making and enhance project outcomes. Your key responsibilities will involve conducting problem formation, data cleaning, and analysis, as well as developing predictive models and machine learning solutions tailored for the engineering and construction sectors. You will collaborate closely with business stakeholders and engineering teams to prioritize innovative ideas and deliver actionable insights through data visualizations and presentations to executive leadership. A strong foundation in statistics, algorithms, and programming—particularly in Python and SQL—is essential for this role, alongside practical experience in machine learning techniques.

Kiewit values data-driven decision-making and fosters an environment of continuous improvement, making it crucial for candidates to possess not only technical expertise but also effective communication skills and the ability to work collaboratively within cross-functional teams. This guide is designed to help you prepare thoroughly for your interview, equipping you with the insights needed to demonstrate your fit for this dynamic and impactful role.

The interview process for a Data Scientist at Kiewit 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 alignment with Kiewit's values.

1. Initial Screening

The process begins with an initial phone screening, usually lasting around 30 minutes. During this call, a recruiter will discuss your background, interests, and motivations for applying to Kiewit. This is also an opportunity for the recruiter to gauge your fit for the company culture and the specific demands of the role. Expect to answer basic questions about your experience and skills, as well as your willingness to travel, which is a significant aspect of the job.

2. Technical Interview

Following the initial screening, candidates may participate in a technical interview, which can be conducted via video conferencing platforms like Teams. This interview typically involves a data scientist or engineer who will ask questions related to your technical expertise, particularly in Python programming, SQL, and machine learning techniques. You may be required to solve problems on the spot or discuss your previous projects and how they relate to the responsibilities of the role.

3. Career Day

Successful candidates are often invited to a "Career Day" event. This unique step allows candidates to meet with various team members across different markets and job titles within Kiewit. During this event, you will engage in one-on-one interviews and have the chance to learn more about the company’s operations and culture. This is also a critical opportunity for you to demonstrate your interpersonal skills and adaptability, as Kiewit values collaboration and communication.

4. Final Interview

The final interview stage may involve multiple rounds, where candidates meet with hiring managers and other stakeholders. This stage focuses on assessing your problem-solving abilities, leadership qualities, and how you handle challenges in a team environment. Expect to discuss your approach to data analysis, predictive modeling, and how you would present your findings to executive leadership.

5. Offer and Negotiation

If you successfully navigate the interview rounds, you may receive a job offer shortly after the final interview. This stage may include discussions about salary, benefits, and other employment terms. Kiewit emphasizes the importance of reliable attendance and the ability to meet deadlines, so be prepared to discuss your work ethic and commitment to the role.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that focus on your technical skills and experiences.

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

Embrace the Company Culture

Kiewit values a strong work ethic and a commitment to teamwork. Be prepared to discuss how you align with these values. Highlight experiences where you demonstrated integrity, collaboration, and a willingness to go the extra mile. Given the emphasis on traveling for work, express your openness to travel and how you view it as an opportunity for growth and learning.

Prepare for a Multi-Stage Interview Process

Expect a structured interview process that may include multiple rounds, such as initial screenings followed by in-depth interviews. Familiarize yourself with the format, as some candidates have reported a "Career Day" where you meet various team members. Use this opportunity to ask insightful questions about the projects and the team dynamics, showcasing your interest in the role and the company.

Showcase Your Technical Skills

As a Data Scientist, proficiency in Python and SQL is crucial. Be ready to discuss your experience with machine learning techniques and data visualization tools. Prepare to explain your approach to problem-solving, data cleaning, and predictive modeling. You may be asked to present your past projects, so have specific examples ready that demonstrate your technical capabilities and how they can be applied to Kiewit’s engineering and construction projects.

Communicate Effectively

Strong communication skills are essential, especially when presenting complex data findings to non-technical stakeholders. Practice articulating your thoughts clearly and concisely. Be prepared to discuss how you would create training materials and conduct in-person training sessions, as this is part of the role. Tailor your communication style to be engaging and informative, ensuring that you can convey technical concepts in an accessible manner.

Be Ready for Behavioral Questions

Expect behavioral interview questions that assess your problem-solving abilities and teamwork. Prepare examples that illustrate how you handled challenges in previous roles, particularly in collaborative settings. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your contributions and the positive outcomes of your actions.

Demonstrate a Growth Mindset

Kiewit is focused on continuous improvement and innovation. Show your enthusiasm for learning and adapting to new technologies and methodologies. Discuss any recent projects or courses you’ve undertaken to enhance your skills, particularly in machine learning and data analysis. This will demonstrate your commitment to personal and professional growth, aligning with Kiewit’s mission to be a data-driven organization.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the role and briefly mention a key point from your discussion that resonated with you. This not only shows your professionalism but also keeps you top of mind as they make their hiring decision.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically proficient but also a great cultural fit for Kiewit. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Kiewit. The interview process will likely focus on your technical skills, problem-solving abilities, and how well you can communicate complex data-driven insights to stakeholders. Be prepared to discuss your experience with machine learning, data analysis, and your ability to work collaboratively in a team environment.

Technical Skills

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

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category. Highlight the importance of choosing the right approach based on the problem at hand.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as using linear regression for predicting house prices. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like 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.

How to Answer

Discuss the project scope, your role, the challenges encountered, and how you overcame them. Emphasize the impact of your work.

Example

“I worked on a predictive maintenance model for a manufacturing client. One challenge was dealing with missing data, which I addressed by implementing imputation techniques. The final model reduced downtime by 20%, significantly improving operational efficiency.”

3. What machine learning algorithms are you most comfortable with, and why?

This question gauges your familiarity with various algorithms and their applications.

How to Answer

Mention specific algorithms, your experience with them, and the contexts in which you applied them.

Example

“I am most comfortable with decision trees and gradient boosting. I find decision trees intuitive for understanding feature importance, while gradient boosting provides robust performance for complex datasets, as I used it in a recent project to predict customer churn.”

4. How do you handle overfitting in a machine learning model?

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

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Explain how you would apply these methods in practice.

Example

“To handle overfitting, I typically use cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 or L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

5. Can you explain how you would deploy a machine learning model to production?

This question assesses your knowledge of the deployment process and best practices.

How to Answer

Outline the steps involved in deploying a model, including testing, monitoring, and updating the model as needed.

Example

“I would start by containerizing the model using Docker for consistency across environments. After thorough testing, I would deploy it to a cloud platform like Azure, ensuring I set up monitoring to track performance and retrain the model periodically based on new data.”

Data Analysis and Visualization

1. What tools do you use for data cleaning and preprocessing?

This question evaluates your familiarity with data preparation techniques.

How to Answer

Mention specific tools and libraries you have used, and explain their importance in the data analysis process.

Example

“I primarily use Python libraries like Pandas for data manipulation and cleaning. I also utilize NumPy for numerical operations and Scikit-learn for preprocessing tasks like scaling and encoding categorical variables.”

2. How do you visualize data to communicate findings effectively?

This question assesses your ability to present data insights clearly.

How to Answer

Discuss the visualization tools you use and the types of visualizations you find most effective for different data types.

Example

“I often use Matplotlib and Seaborn for creating visualizations. For instance, I prefer scatter plots to show relationships between variables and bar charts for categorical comparisons, as they provide clear insights to stakeholders.”

3. Can you describe a time when your data analysis led to a significant business decision?

This question looks for evidence of your impact on business outcomes.

How to Answer

Share a specific example, detailing the analysis performed, the decision made, and the results achieved.

Example

“In a previous role, I analyzed sales data and identified a trend indicating a decline in a specific product line. My analysis led to a strategic decision to revamp the marketing approach, resulting in a 30% increase in sales over the next quarter.”

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

This question assesses your database management skills.

How to Answer

Discuss your proficiency with SQL and provide examples of how you have used it to extract and manipulate data.

Example

“I have extensive experience with SQL for querying databases. In my last project, I wrote complex queries to join multiple tables and aggregate data, which was crucial for generating insights for our marketing team.”

5. How do you ensure data quality in your analyses?

This question evaluates your attention to detail and commitment to accuracy.

How to Answer

Discuss the methods you use to validate and clean data before analysis.

Example

“I ensure data quality by implementing validation checks during the data collection process and performing exploratory data analysis to identify anomalies. I also use automated scripts to flag inconsistencies, ensuring the data is reliable for analysis.”

Behavioral Questions

1. Describe a time when you had to work with a difficult team member. How did you handle it?

This question assesses your interpersonal skills and ability to work in a team.

How to Answer

Share a specific situation, focusing on your approach to resolving the conflict and the outcome.

Example

“I once worked with a team member who was resistant to feedback. I scheduled a one-on-one meeting to discuss our project goals and listened to their concerns. By fostering open communication, we were able to align our efforts and improve collaboration.”

2. How do you prioritize your tasks when working on multiple projects?

This question evaluates your time management skills.

How to Answer

Discuss your approach to prioritization and any tools or methods you use to stay organized.

Example

“I prioritize tasks based on deadlines and project impact. I use project management tools like Trello to track progress and ensure I allocate time effectively, allowing me to meet deadlines without compromising quality.”

3. Can you give an example of a time when you had to present complex information to a non-technical audience?

This question assesses your communication skills.

How to Answer

Share an example that highlights your ability to simplify complex concepts for diverse audiences.

Example

“I once presented a machine learning model’s results to a group of executives. I focused on the business implications rather than technical details, using visualizations to illustrate key points, which helped them understand the value of the model in decision-making.”

4. What motivates you to work in the data science field?

This question gauges your passion for the role.

How to Answer

Discuss your interest in data science and how it aligns with your career goals.

Example

“I am motivated by the potential of data to drive impactful decisions. The ability to uncover insights that can transform business strategies excites me, and I am eager to contribute to projects that make a difference in the engineering and construction industries.”

5. How do you stay updated with the latest trends and technologies in data science?

This question assesses your commitment to continuous learning.

How to Answer

Mention specific resources, communities, or courses you engage with to keep your skills current.

Example

“I regularly read industry blogs, participate in online courses, and attend webinars to stay informed about the latest trends. I also engage with data science communities on platforms like LinkedIn and GitHub to exchange knowledge and best practices.”