PG&E Data Scientist Interview Questions + Guide in 2025

PG&E is a leading energy company dedicated to delivering safe and reliable energy while transitioning to a sustainable future.

As a Data Scientist at PG&E, you will play a pivotal role in the Data Science & Artificial Intelligence Department, where your expertise will be essential in developing innovative data-driven solutions. Your key responsibilities will include designing and implementing machine learning models to analyze large datasets, particularly focusing on energy consumption patterns and predicting customer behavior regarding electric vehicle adoption. You will collaborate with cross-functional teams, including data engineers and business experts, to ensure the successful delivery of impactful data products that align with PG&E's mission of promoting safety, reliability, and sustainability. A strong foundation in statistics, programming (especially in Python), and experience with cloud computing platforms are critical for this role. Candidates who possess excellent analytical, problem-solving, and communication skills, along with a passion for leveraging data to drive organizational change, will thrive in this dynamic environment.

This guide will provide you with valuable insights and tailored questions to help you prepare effectively for your interview, ensuring you can showcase your skills and fit for the role at PG&E.

The interview process for a Data Scientist role at PG&E is structured and thorough, designed to assess both technical and behavioral competencies. Candidates can expect a multi-step process that includes several rounds of interviews, each focusing on different aspects of their qualifications and fit for the company.

1. Initial Phone Screening

The process typically begins with a phone screening conducted by a recruiter. This initial conversation lasts about 30 minutes and serves to gather basic information about your background, education, and work experience. The recruiter will also discuss the role and the company culture, ensuring that candidates understand PG&E's values, particularly around safety, reliability, and sustainability.

2. Technical Interview

Following the initial screening, candidates will participate in a technical interview, which may be conducted via video conferencing. This interview focuses on assessing your technical skills, particularly in programming languages such as Python and SQL. Expect to solve coding problems or discuss your experience with data science methodologies, including model building and evaluation. You may also be asked to describe past projects, particularly those involving data pipelines or machine learning models.

3. Panel Interview

The next step is usually a panel interview, which involves multiple interviewers from different departments. This round is more in-depth and typically lasts about an hour. Interviewers will ask a mix of behavioral and situational questions, often using the STAR method to evaluate how you handle various work scenarios. Questions may revolve around teamwork, conflict resolution, and your approach to problem-solving in complex projects.

4. Final Interview with Hiring Manager

The final stage often includes a one-on-one interview with the hiring manager. This interview may delve deeper into your technical expertise and how your skills align with PG&E's strategic goals. You may be asked to discuss specific projects from your resume and how they relate to the responsibilities of the Data Scientist role. Additionally, this is an opportunity for you to ask questions about the team dynamics and the company's future direction.

Throughout the process, candidates should be prepared for a focus on PG&E's mission and values, as well as a strong emphasis on collaboration and communication skills.

Now that you have an understanding of the interview process, let's explore the types of questions you might encounter during your interviews.

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

Understand PG&E's Mission and Values

Familiarize yourself with PG&E's commitment to safety, reliability, affordability, and sustainability. As a Data Scientist, you will be expected to align your work with these values. Be prepared to discuss how your past experiences and projects can contribute to PG&E's mission of transitioning to a sustainable grid. Highlight any relevant projects that demonstrate your understanding of these principles.

Prepare for Behavioral Questions

Expect a significant focus on behavioral questions during your interviews. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on your past experiences, particularly those that showcase your problem-solving skills, teamwork, and ability to handle conflict. Be ready to discuss specific instances where you led projects, faced challenges, or contributed to team success.

Brush Up on Technical Skills

Given the technical nature of the role, ensure you are well-versed in Python, SQL, and machine learning concepts. Be prepared to discuss your experience with data pipelines, statistical modeling, and algorithm development. You may be asked to explain your approach to building machine learning models or to provide examples of how you've handled large datasets. Practicing coding problems and reviewing relevant technical concepts will be beneficial.

Emphasize Cross-Functional Collaboration

PG&E values collaboration across various departments. Be ready to discuss your experience working with cross-functional teams, including data engineers, subject matter experts, and business partners. Highlight how you have effectively communicated technical information to non-technical stakeholders and how you have gathered business requirements to inform your data science projects.

Showcase Your Analytical Mindset

Demonstrate your analytical skills by discussing your approach to exploratory data analysis (EDA) and model validation. Be prepared to explain how you would conduct root-cause analysis and error analysis on your models. Providing examples of how you have used data to drive decision-making in previous roles will help illustrate your analytical mindset.

Be Ready for Situational Questions

Expect situational questions that assess your ability to handle real-world challenges. Prepare to discuss how you would approach specific scenarios related to data science projects, such as predicting customer behavior or optimizing resource allocation. Think about how you would apply your technical skills to solve practical problems within the utility industry.

Communicate Your Passion for Innovation

PG&E is focused on innovation in the utility sector. Share your enthusiasm for emerging technologies and data-driven solutions. Discuss any relevant projects or research that demonstrate your commitment to advancing the field of data science, particularly in areas related to energy consumption, electric vehicles, or sustainability.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the position and briefly mention how your skills align with PG&E's goals. This not only shows professionalism but also reinforces your enthusiasm for the role.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at PG&E. The interview process will likely assess your technical skills, problem-solving abilities, and how well you align with the company's values, particularly in relation to sustainability and innovation in the utility sector. Be prepared to discuss your past experiences, technical knowledge, and how you can contribute to PG&E's mission.

Technical Skills

1. Can you describe a data pipeline you have built in the past?

This question aims to assess your practical experience in developing data pipelines, which is crucial for a Data Scientist role.

How to Answer

Discuss the architecture of the pipeline, the tools you used, and the specific challenges you faced during its development.

Example

“I built a data pipeline using Apache Airflow to automate the extraction of customer usage data from our SQL database. I implemented data cleaning and transformation steps using Python, which improved our data quality and reduced processing time by 30%.”

2. How do you approach feature engineering for a machine learning model?

This question evaluates your understanding of one of the most critical aspects of model development.

How to Answer

Explain your process for identifying and creating features that can improve model performance, including any techniques or tools you use.

Example

“I start by analyzing the dataset to understand the relationships between variables. I then create new features based on domain knowledge, such as aggregating customer usage data over time, and use techniques like one-hot encoding for categorical variables to enhance the model's predictive power.”

3. What statistical methods do you use to validate your machine learning models?

This question tests your knowledge of model evaluation techniques.

How to Answer

Discuss the statistical methods you apply, such as cross-validation, confusion matrices, or ROC curves, and why they are important.

Example

“I typically use k-fold cross-validation to ensure that my model generalizes well to unseen data. Additionally, I analyze the confusion matrix to understand the model's performance across different classes, which helps in fine-tuning the model further.”

4. Describe a time when you had to analyze a large dataset. What tools did you use?

This question assesses your experience with big data and the tools you are familiar with.

How to Answer

Mention the dataset, the tools you used, and the insights you derived from your analysis.

Example

“I worked on a project analyzing customer energy consumption data from over a million households. I used Python with Pandas for data manipulation and visualization libraries like Matplotlib to present my findings, which revealed significant trends in energy usage during peak hours.”

5. How do you handle missing data in your datasets?

This question evaluates your data preprocessing skills.

How to Answer

Explain the strategies you use to deal with missing data, including imputation methods or data removal.

Example

“I assess the extent of missing data first. If it’s minimal, I might use mean or median imputation. For larger gaps, I consider using predictive models to estimate missing values or, if appropriate, remove those records entirely to maintain data integrity.”

Behavioral Questions

1. Tell me about a time you faced a significant challenge in a project. How did you overcome it?

This question aims to understand your problem-solving and resilience.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response.

Example

“In a previous project, we faced a major setback when our data source became unavailable. I quickly coordinated with the team to identify alternative data sources and adjusted our analysis plan, which allowed us to meet our deadline without compromising quality.”

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

This question assesses your time management skills.

How to Answer

Discuss your approach to prioritization, including any tools or methods you use.

Example

“I use a combination of project management tools like Trello and the Eisenhower Matrix to prioritize tasks based on urgency and importance. This helps me focus on high-impact activities while ensuring that I meet deadlines across multiple projects.”

3. Describe a situation where you had to communicate complex technical information to a non-technical audience.

This question evaluates your communication skills.

How to Answer

Explain how you simplify complex concepts and ensure understanding.

Example

“I once presented a machine learning model's results to a group of stakeholders. I used visual aids to illustrate key points and avoided jargon, focusing instead on the implications of the findings for their decision-making process, which led to a productive discussion.”

4. Can you give an example of how you have worked in a cross-functional team?

This question assesses your collaboration skills.

How to Answer

Highlight your experience working with diverse teams and how you contributed to the project.

Example

“I collaborated with data engineers and business analysts on a project to optimize energy distribution. I facilitated regular meetings to ensure alignment on goals and shared insights from my analysis, which helped us develop a more effective solution.”

5. How do you ensure that your work aligns with company values, particularly regarding sustainability?

This question gauges your alignment with PG&E’s mission.

How to Answer

Discuss how you incorporate sustainability into your work and decision-making processes.

Example

“I prioritize projects that focus on energy efficiency and renewable resources. For instance, in my last role, I developed a model to predict the adoption of solar energy solutions, which directly supports sustainability initiatives and aligns with my personal values.”