FirstEnergy Data Scientist Interview Questions + Guide in 2025

FirstEnergy is a forward-thinking electric utility dedicated to safety, reliability, and operational excellence, operating one of the nation's largest investor-owned electric systems.

As a Data Scientist at FirstEnergy, you will be instrumental in leveraging machine learning, statistical methods, and artificial intelligence to address critical business questions and drive actionable insights across various departments. Key responsibilities include collaborating with data engineers and other data scientists within agile scrum squads to develop predictive models, identify patterns within complex datasets, and deploy scalable solutions. A successful candidate will possess a strong background in statistics and machine learning, with experience in algorithms such as decision trees and neural networks, as well as proficiency in programming languages like Python or R. Ideal traits include a passion for digital innovation, effective communication skills to explain complex analytical concepts to non-technical stakeholders, and the ability to foster collaboration across cross-functional teams.

This guide is designed to help you prepare thoroughly for your interview by focusing on the essential skills and experiences that align with FirstEnergy's values and business operations.

The interview process for a Data Scientist position at FirstEnergy is structured to assess both technical and behavioral competencies, ensuring candidates align with the company's values and operational needs.

1. Initial Screening

The process typically begins with a brief phone screening conducted by a recruiter. This initial conversation lasts around 20 minutes and focuses on your resume, relevant experiences, and general fit for the company culture. The recruiter may also provide an overview of the role and the expectations associated with it.

2. Technical Assessment

Following the initial screening, candidates may be required to complete a technical assessment. This could involve a practical exercise, such as a coding challenge or a statistical analysis task, designed to evaluate your proficiency in relevant programming languages (like Python) and statistical techniques. The assessment is usually time-bound and aims to gauge your problem-solving skills and technical knowledge.

3. Panel Interview

Candidates who successfully pass the technical assessment will move on to a panel interview. This stage typically involves a group of three to four interviewers, including HR representatives and potential team members. The panel interview lasts about 45 minutes to an hour and employs the STAR (Situation, Task, Action, Result) method to frame behavioral questions. Expect inquiries about past experiences, teamwork, and how you handle challenges, such as "Describe a time you worked in a team" or "Tell us about a situation where you prioritized safety at work."

4. Final Interview

If you advance past the panel interview, you may have a final one-on-one interview with a senior manager or director. This session is often more conversational and allows for a deeper discussion about your experiences, motivations, and how you can contribute to FirstEnergy's goals. It may also include some technical questions related to machine learning models and statistical methods, as well as your understanding of the utility sector.

5. Offer and Onboarding

Candidates who successfully navigate the interview process will receive an offer. The onboarding process will follow, where you will be introduced to the company's culture, policies, and your specific role within the team.

As you prepare for your interview, consider the types of questions that may arise during each stage of the process.

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

Prepare for Behavioral Questions Using the STAR Method

FirstEnergy places a strong emphasis on behavioral questions during the interview process. Familiarize yourself with the STAR (Situation, Task, Action, Result) method to structure your responses effectively. Be ready to discuss specific situations where you demonstrated teamwork, problem-solving, and adaptability. Given the company's focus on safety and operational excellence, prepare examples that highlight your commitment to these values.

Understand the Technical Requirements

As a Data Scientist, you will be expected to have a solid grasp of statistics, algorithms, and machine learning. Brush up on your knowledge of statistical techniques, including regression analysis and evaluation metrics like F1 score and ROC AUC. Be prepared to discuss your experience with programming languages such as Python and how you have applied these skills in previous projects. Expect technical questions that assess your understanding of model validation and performance metrics.

Emphasize Collaboration and Communication Skills

FirstEnergy values teamwork and cross-functional collaboration. Be prepared to discuss how you have worked effectively in teams, particularly in agile environments. Highlight your ability to communicate complex analytical concepts to non-technical stakeholders, as this will be crucial in your role. Consider sharing examples of how you have guided colleagues or led research efforts in previous positions.

Research the Company Culture and Values

Understanding FirstEnergy's commitment to safety, reliability, and community engagement will help you align your responses with their core values. Familiarize yourself with recent initiatives or projects the company has undertaken, particularly those related to digital enablement and customer satisfaction. This knowledge will not only demonstrate your interest in the company but also allow you to articulate how your skills and experiences can contribute to their mission.

Be Ready for a Panel Interview Format

Many candidates have reported experiencing panel interviews with multiple interviewers. Prepare for this by practicing your responses to common questions and being ready to engage with different interviewers simultaneously. This format can be intense, so stay calm and focused, ensuring that you address each interviewer’s questions thoughtfully.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. This is also a chance to reiterate your enthusiasm for the role and the company. If you don’t hear back within a reasonable timeframe, consider following up politely to inquire about the status of your application. This demonstrates your continued interest and professionalism.

By following these tailored tips, you can approach your interview with confidence and a clear understanding of what FirstEnergy is looking for in a Data Scientist. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at FirstEnergy. The interview process will likely focus on a combination of behavioral and technical questions, particularly emphasizing statistical methods, machine learning, and problem-solving abilities. Familiarize yourself with the STAR method for answering behavioral questions, as it is commonly used in this interview format.

Behavioral Questions

1. Describe a time when you had to work in a team to achieve a goal.

Collaboration is key in a data science role, especially when working in agile scrum squads.

How to Answer

Focus on your role within the team, the challenges faced, and how you contributed to the team's success.

Example

“In my previous role, I was part of a team tasked with developing a predictive model for customer churn. I facilitated communication between data engineers and business stakeholders, ensuring everyone was aligned on objectives. Our collaborative efforts led to a model that improved retention rates by 15%.”

2. Tell me about a time you dealt with a difficult stakeholder.

Understanding how to manage relationships is crucial in a cross-functional environment.

How to Answer

Discuss the situation, your approach to resolving the conflict, and the outcome.

Example

“I once worked with a stakeholder who was skeptical about the data-driven approach we proposed. I organized a meeting to present our findings and addressed their concerns directly. By providing clear data visualizations and demonstrating the potential impact, I was able to gain their support for the project.”

3. Can you give an example of when you prioritized safety in your work?

Safety is a core value at FirstEnergy, and they will want to see your commitment to it.

How to Answer

Share a specific instance where you took steps to ensure safety in your work environment.

Example

“While working on a project that involved field data collection, I implemented a safety protocol that included regular check-ins and equipment inspections. This not only ensured the safety of the team but also improved our data collection efficiency.”

4. Describe a situation where you had to adapt to a significant change at work.

Adaptability is essential in a fast-paced environment.

How to Answer

Highlight your flexibility and how you managed the transition.

Example

“When our team shifted to remote work due to the pandemic, I quickly adapted by setting up regular virtual meetings and utilizing collaboration tools. This helped maintain team cohesion and ensured we met our project deadlines.”

5. Tell us about a time you went above and beyond in your role.

This question assesses your work ethic and commitment to excellence.

How to Answer

Provide a specific example that showcases your initiative and dedication.

Example

“I noticed that our data processing was taking longer than necessary, which affected project timelines. I took the initiative to streamline the process by automating certain tasks, which reduced processing time by 30% and allowed the team to focus on more complex analyses.”

Technical Questions

1. Explain the difference between precision and recall.

Understanding evaluation metrics is crucial for a data scientist.

How to Answer

Define both terms clearly and explain their significance in model evaluation.

Example

“Precision measures the accuracy of positive predictions, while recall measures the ability to identify all relevant instances. In a medical diagnosis model, high precision means fewer false positives, while high recall means fewer false negatives, both of which are critical depending on the context.”

2. How would you approach building a predictive model?

This question assesses your methodology and thought process.

How to Answer

Outline the steps you would take, from data collection to model evaluation.

Example

“I would start by defining the problem and gathering relevant data. Next, I would preprocess the data, perform exploratory data analysis, and select appropriate features. After that, I would choose a suitable algorithm, train the model, and evaluate its performance using metrics like F1 score and ROC AUC.”

3. Describe a machine learning algorithm you have implemented and its impact.

This question gauges your practical experience with machine learning.

How to Answer

Discuss the algorithm, the problem it addressed, and the results achieved.

Example

“I implemented a random forest algorithm to predict equipment failures in our utility network. By analyzing historical data, we were able to reduce unplanned outages by 20%, significantly improving service reliability.”

4. What statistical techniques do you find most useful in your work?

This question assesses your knowledge of statistics.

How to Answer

Mention specific techniques and their applications in data analysis.

Example

“I frequently use linear regression for predictive modeling and t-tests for hypothesis testing. These techniques help me understand relationships between variables and validate assumptions in my analyses.”

5. How do you ensure the quality of your data before analysis?

Data quality is critical for accurate insights.

How to Answer

Discuss your approach to data validation and cleaning.

Example

“I perform thorough data validation checks, including identifying missing values, outliers, and inconsistencies. I also use data profiling techniques to understand the data distribution and ensure it meets the requirements for analysis.”