Exelon Data Scientist Interview Questions + Guide in 2025

Exelon is at the forefront of the energy transformation, dedicated to powering a cleaner and more resilient future through innovative solutions.

As a Data Scientist at Exelon, you will harness the power of data to drive strategic decision-making and improvements across various business units. Your core responsibilities will include applying advanced analytics techniques to analyze complex datasets, developing predictive models, and collaborating closely with stakeholders to translate business needs into actionable insights. A successful candidate will possess a strong background in machine learning, statistical modeling, and data manipulation, along with proficiency in programming languages such as Python or R. Furthermore, a commitment to continuous learning and the ability to effectively communicate technical findings to non-technical audiences will align perfectly with Exelon's values of integrity, innovation, and community service.

This guide is designed to equip you with the knowledge and insights needed to excel in your interview for the Data Scientist role at Exelon, enabling you to confidently demonstrate your fit within the company and its mission.

The interview process for a Data Scientist role at Exelon is structured and thorough, designed to assess both technical skills and cultural fit within the organization. The process typically unfolds in several key stages:

1. Initial Screening

The first step usually involves a brief phone call with a recruiter. This conversation lasts around 10-15 minutes and focuses on your background, salary expectations, and basic qualifications for the role. The recruiter will gauge your interest in the position and provide an overview of the company culture and expectations.

2. Technical Assessment

Following the initial screening, candidates may be required to complete a technical assessment. This could involve a one-way video interview where you answer a set of predetermined questions related to your technical skills, such as data analysis techniques, programming languages (like Python or R), and statistical modeling. Some candidates have also reported completing a memory or concentration assessment as part of this stage.

3. Panel Interview

The next phase typically consists of a panel interview, which can last about 60 minutes. This interview is conducted via video conferencing tools like Microsoft Teams and involves multiple interviewers, often including team members and managers. Expect a mix of behavioral and technical questions, with a strong emphasis on the STAR (Situation, Task, Action, Result) format. Interviewers will be interested in your past experiences, problem-solving abilities, and how you handle conflicting priorities or challenges in a team setting.

4. In-Person or Final Interview

In some cases, candidates may be invited for an in-person interview, especially if they are local. This final interview may involve additional team members and could include more in-depth discussions about your technical expertise and how it aligns with Exelon's goals. The focus will be on your ability to communicate complex data insights to non-technical stakeholders and your understanding of the energy sector.

5. Offer and Follow-Up

After the interviews, candidates can expect a follow-up within a few weeks. The communication is generally clear and timely, with feedback provided post-interview. If selected, you will receive an offer, which may include details about salary, benefits, and other incentives.

As you prepare for your interview, it's essential to be ready for a variety of questions that will assess both your technical capabilities and your fit within Exelon's collaborative and innovative culture. Here are some of the types of questions you might encounter during the interview process.

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

Understand the Company’s Mission and Values

Exelon is committed to powering a cleaner, brighter future, and they value innovation, integrity, and community service. Familiarize yourself with their mission and how your role as a Data Scientist can contribute to these goals. Be prepared to discuss how your personal values align with Exelon's and how you can help drive their initiatives forward.

Prepare for Behavioral Questions with the STAR Method

Expect to encounter behavioral questions that require you to demonstrate your problem-solving skills and teamwork abilities. Use the STAR (Situation, Task, Action, Result) method to structure your responses. This approach will help you articulate your experiences clearly and show how you’ve successfully navigated challenges in the past, which is crucial for a role that involves collaboration with various stakeholders.

Brush Up on Technical Skills Relevant to the Role

Given the technical nature of the Data Scientist position, ensure you are well-versed in the tools and languages mentioned in the job description, such as Python, R, and SQL. Be ready to discuss your experience with machine learning algorithms, data mining, and statistical modeling. You may also be asked about your familiarity with big data technologies, so review concepts related to Spark, Hadoop, and data warehousing.

Communicate Clearly and Effectively

Exelon values strong communication skills, especially the ability to translate complex data findings into actionable insights for business stakeholders. Practice explaining technical concepts in layman's terms, as you may need to demonstrate this skill during the interview. Be concise and direct in your responses, ensuring that you convey your points effectively.

Showcase Your Problem-Solving Abilities

Expect questions that assess your analytical thinking and problem-solving skills. Be prepared to discuss specific examples of how you have approached data-intensive business problems in the past. Highlight your ability to develop predictive models and your experience in using advanced analytics techniques to drive business improvements.

Be Ready for a Panel Interview Format

Interviews at Exelon may involve a panel of interviewers. Approach this with confidence and engage with each panel member. Make eye contact, address each person when responding, and be mindful of the dynamics in the room. This will demonstrate your ability to collaborate and communicate effectively in a team setting.

Emphasize Continuous Learning and Development

Exelon values professionals who are committed to continuous learning. Be prepared to discuss how you stay current with industry trends, new technologies, and best practices in data science. Mention any relevant courses, certifications, or projects that showcase your dedication to professional growth.

Prepare for Questions on Handling Conflicting Priorities

Given the nature of the role, you may be asked how you would handle conflicting priorities from different stakeholders. Think of examples from your past experiences where you successfully managed competing demands and how you communicated with your team and leadership to find a resolution.

Follow Up with Thoughtful Questions

At the end of the interview, you will likely have the opportunity to ask questions. Use this time to inquire about the team dynamics, ongoing projects, and how the Data Scientist role contributes to Exelon's strategic goals. This shows your genuine interest in the position and helps you assess if the company is the right fit for you.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at Exelon. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Exelon. The interview process will likely assess your technical skills in data analysis, machine learning, and statistical modeling, as well as your ability to communicate complex findings to stakeholders. Be prepared to discuss your experiences and how they relate to the role, using the STAR (Situation, Task, Action, Result) method to structure your responses.

Technical Skills

1. What machine learning algorithms are you most familiar with, and how have you applied them in your previous work?

Understanding the algorithms you’ve used and their applications is crucial for this role.

How to Answer

Discuss specific algorithms, their use cases, and the outcomes of your projects. Highlight any unique challenges you faced and how you overcame them.

Example

“I have extensive experience with decision trees and random forests, which I used in a project to predict customer churn. By analyzing historical data, I was able to identify key factors influencing churn and implemented a model that improved retention rates by 15%.”

2. Can you explain the process of feature engineering and its importance in machine learning?

Feature engineering is a critical step in building effective models.

How to Answer

Describe the steps involved in feature engineering and provide examples of how it has improved model performance in your past projects.

Example

“Feature engineering involves selecting, modifying, or creating new features from raw data to improve model accuracy. In a recent project, I transformed categorical variables into numerical formats and created interaction terms, which led to a 20% increase in model performance.”

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

Handling missing data is a common challenge in data science.

How to Answer

Discuss various techniques you’ve used to address missing data, such as imputation or removal, and the rationale behind your choices.

Example

“I typically assess the extent of missing data and choose imputation methods based on the data type. For instance, I used mean imputation for numerical data in one project, which preserved the dataset size while minimizing bias.”

4. Describe your experience with big data technologies. Which tools have you used?

Familiarity with big data tools is essential for this role.

How to Answer

Mention specific tools and technologies you’ve worked with, and describe how they contributed to your projects.

Example

“I have worked extensively with Apache Spark for processing large datasets. In a project analyzing energy consumption patterns, Spark allowed me to efficiently process terabytes of data, leading to actionable insights for operational improvements.”

5. What statistical methods do you commonly use in your analyses?

Statistical knowledge is fundamental for a data scientist.

How to Answer

List the statistical methods you are proficient in and provide examples of how you’ve applied them in your work.

Example

“I frequently use regression analysis and hypothesis testing. For example, I conducted a regression analysis to determine the impact of various factors on energy consumption, which helped inform our marketing strategies.”

Behavioral Questions

1. Describe a time when you had to communicate complex data findings to a non-technical audience.

Effective communication is key in this role.

How to Answer

Share a specific instance where you simplified complex data for stakeholders, focusing on the methods you used to ensure understanding.

Example

“I presented a data analysis report to our marketing team, where I used visualizations to illustrate trends. By focusing on key insights and avoiding technical jargon, I ensured everyone understood the implications for our strategy.”

2. How do you prioritize tasks when working on multiple projects with tight deadlines?

Time management is crucial in a fast-paced environment.

How to Answer

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

Example

“I use a combination of project management tools and prioritization frameworks like the Eisenhower Matrix. This helps me focus on urgent and important tasks, ensuring that I meet deadlines without compromising quality.”

3. Tell me about a challenging project you worked on and how you overcame the obstacles.

This question assesses your problem-solving skills.

How to Answer

Describe the project, the challenges faced, and the steps you took to resolve them.

Example

“In a project analyzing customer feedback, I faced challenges with data quality. I implemented a rigorous data cleaning process, which involved standardizing formats and removing duplicates, ultimately leading to more reliable insights.”

4. How do you ensure that your work aligns with the business goals of your team?

Alignment with business objectives is essential for a data scientist.

How to Answer

Explain your approach to understanding business needs and how you incorporate them into your analyses.

Example

“I regularly engage with stakeholders to understand their goals and challenges. By aligning my analyses with their objectives, I ensure that my insights are relevant and actionable, which enhances the impact of my work.”

5. Why do you want to work at Exelon?

This question gauges your interest in the company and its mission.

How to Answer

Discuss your alignment with Exelon’s values and how you see yourself contributing to their goals.

Example

“I admire Exelon’s commitment to sustainability and innovation in the energy sector. I believe my skills in data science can help drive insights that support your mission of powering a cleaner, brighter future.”