General Motors Data Scientist Interview Questions + Guide in 2025

General Motors is a global leader in the automotive industry, committed to innovation and sustainability with a vision of a world with Zero Crashes, Zero Emissions, and Zero Congestion.

The Data Scientist role at General Motors encompasses a crucial position within a dynamic and diverse team that applies advanced statistical and machine learning methodologies to solve complex business challenges. Key responsibilities include developing predictive and prescriptive models, analyzing large datasets to uncover insights, and creating data-driven solutions that impact various business sectors, including marketing, operations, and customer experience. The ideal candidate will possess strong skills in Python and SQL, along with expertise in machine learning algorithms and data visualization tools. A collaborative mindset is essential, as the role requires working closely with cross-functional teams to translate business needs into analytical strategies.

This guide will prepare you to effectively showcase your technical skills and problem-solving abilities, while also aligning your experiences with GM’s core values of innovation and inclusivity.

The interview process for a Data Scientist position at General Motors is structured and involves multiple stages designed to assess both technical and interpersonal skills.

1. Initial Recruiter Screen

The first step in the interview process is a phone screening with a recruiter. This conversation typically lasts around 30 minutes and focuses on your background, experiences, and motivations for applying to General Motors. The recruiter will provide insights into the company culture and the specific responsibilities of the Data Scientist role. This is also an opportunity for you to ask questions about the position and the team dynamics.

2. HireVue Interview

Following the initial screen, candidates are often invited to complete a HireVue interview. This is a recorded video interview where you will respond to a series of behavioral questions. You will have a limited time to prepare your answers, and you can record your responses multiple times, but only the last recording will be submitted. The questions typically revolve around your past experiences, problem-solving abilities, and how you handle challenges in a team setting.

3. Technical Interviews

The next phase usually consists of one or two technical interviews, which may be conducted via video call. These interviews are typically led by senior data scientists or managers and last about an hour each. During these sessions, you will be asked to discuss your previous projects in detail, including the methodologies you employed and the outcomes achieved. Expect to answer technical questions related to machine learning, data analysis, and statistical methods, as well as to demonstrate your proficiency in programming languages such as Python and SQL.

4. Behavioral Interviews

In addition to technical assessments, candidates will also undergo behavioral interviews. These interviews focus on your soft skills, teamwork, and cultural fit within the organization. You may be asked to provide examples of how you have navigated conflicts, made decisions under pressure, or contributed to team success. The STAR (Situation, Task, Action, Result) method is often recommended for structuring your responses.

5. Final Interview

The final stage may involve a more in-depth discussion with higher-level management or cross-functional team members. This interview is designed to assess your strategic thinking and how you can contribute to GM’s broader goals. You may be asked to present a case study or a project you have worked on, showcasing your analytical skills and ability to communicate complex information effectively.

As you prepare for your interviews, it’s essential to be ready for a mix of technical and behavioral questions that reflect the diverse challenges you may face in the role. Next, let’s explore some of the specific interview questions that candidates have encountered during the process.

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

Understand the Interview Structure

The interview process at General Motors typically involves multiple rounds, including a recruiter screen, a HireVue recorded interview, and one or two technical interviews with team members. Familiarize yourself with this structure so you can prepare accordingly. Be ready to discuss your past projects in detail, as interviewers often focus on your specific experiences and the methodologies you employed.

Prepare for Behavioral Questions

Expect a significant portion of the interview to focus on behavioral questions. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on your past experiences, particularly those that demonstrate your problem-solving skills, teamwork, and ability to handle conflict. Given the emphasis on collaboration at GM, be prepared to discuss how you’ve worked effectively in teams and navigated challenges.

Showcase Your Technical Expertise

As a Data Scientist, you will be expected to demonstrate a strong command of various technical skills, including Python, SQL, and machine learning methodologies. Be prepared to discuss specific projects where you applied these skills, including the rationale behind your chosen methods. Interviewers may ask you to explain why you selected a particular approach over others, so be ready to articulate your thought process clearly.

Emphasize Business Acumen

General Motors values candidates who can connect technical solutions to business outcomes. Be prepared to discuss how your analytical work has driven business decisions or improved processes in previous roles. Understanding GM’s business model and how data science can contribute to their goals will give you an edge.

Be Ready for Case Studies

Some interviews may include case studies or problem-solving exercises. Practice analyzing data sets and presenting your findings in a clear, concise manner. Focus on how you would approach a real-world problem relevant to GM’s operations, and be prepared to discuss your methodology and the implications of your findings.

Communicate Effectively

Strong communication skills are essential for this role. Practice explaining complex technical concepts in simple terms, as you may need to present your findings to non-technical stakeholders. Be clear and confident in your delivery, and ensure you can tailor your communication style to different audiences.

Follow Up Professionally

After your interviews, send a thank-you note to your interviewers. Express your appreciation for the opportunity to interview and reiterate your enthusiasm for the role. This not only shows professionalism but also reinforces your interest in the position.

Align with Company Culture

General Motors emphasizes inclusion and collaboration. Research their values and culture, and think about how your personal values align with theirs. Be prepared to discuss how you can contribute to a positive team environment and support GM’s vision of a world with Zero Crashes, Zero Emissions, and Zero Congestion.

By following these tips and preparing thoroughly, you can present yourself as a strong candidate for the Data Scientist role at General Motors. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at General Motors. The interview process will likely assess both your technical skills and your ability to work collaboratively within a team. Be prepared to discuss your past projects in detail, as well as your approach to problem-solving and data analysis.

Machine Learning

1. How would you approach a machine learning problem from start to finish?

This question assesses your understanding of the data science lifecycle and your ability to apply it in practice.

How to Answer

Outline the steps you would take, including problem definition, data collection, preprocessing, model selection, training, evaluation, and deployment. Emphasize your ability to iterate on the model based on feedback and results.

Example

“I would start by clearly defining the problem and understanding the business objectives. Next, I would gather relevant data, ensuring it is clean and well-structured. After preprocessing the data, I would select appropriate machine learning algorithms, train the models, and evaluate their performance using metrics relevant to the business goals. Finally, I would deploy the model and monitor its performance, making adjustments as necessary.”

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

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Define both terms and provide examples of algorithms used in each category. Highlight scenarios where each type is applicable.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as classification and regression tasks. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like clustering and dimensionality reduction. For instance, K-means clustering is a common unsupervised learning algorithm.”

3. Describe a machine learning project you worked on and the challenges you faced.

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Discuss the project’s objectives, your role, the methodologies used, and specific challenges encountered, along with how you overcame them.

Example

“In a recent project, I developed a predictive model for customer churn. One challenge was dealing with imbalanced data, which I addressed by using techniques like SMOTE for oversampling the minority class. This improved the model’s accuracy and provided better insights into customer retention strategies.”

4. What techniques do you use for feature selection?

This question evaluates your understanding of model optimization and data preprocessing.

How to Answer

Discuss various techniques such as recursive feature elimination, LASSO regression, and tree-based methods. Explain why feature selection is important.

Example

“I often use recursive feature elimination to systematically remove features and assess model performance. Additionally, I find LASSO regression useful for both feature selection and regularization, as it can shrink less important feature coefficients to zero, simplifying the model.”

5. How do you handle overfitting in your models?

This question assesses your knowledge of model evaluation and improvement techniques.

How to Answer

Explain various strategies to prevent overfitting, such as cross-validation, regularization techniques, and simplifying the model.

Example

“To combat overfitting, I employ cross-validation to ensure the model generalizes well to unseen data. I also use regularization techniques like L1 and L2 regularization to penalize overly complex models. Additionally, I monitor the training and validation loss to identify signs of overfitting early on.”

Statistics & Probability

1. Explain the concept of p-value in hypothesis testing.

This question tests your understanding of statistical significance.

How to Answer

Define p-value and its role in hypothesis testing, including what it indicates about the null hypothesis.

Example

“The p-value measures the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that the observed effect is statistically significant.”

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

This question assesses your grasp of fundamental statistical concepts.

How to Answer

Explain the theorem and its implications for sampling distributions and inferential statistics.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population’s distribution. This is crucial because it allows us to make inferences about population parameters using sample statistics, even when the population distribution is unknown.”

3. How do you assess the normality of a dataset?

This question evaluates your knowledge of data analysis techniques.

How to Answer

Discuss various methods for assessing normality, such as visual inspections (histograms, Q-Q plots) and statistical tests (Shapiro-Wilk test).

Example

“I assess normality by first visualizing the data using histograms and Q-Q plots to check for deviations from a straight line. Additionally, I apply the Shapiro-Wilk test, where a p-value greater than 0.05 indicates that the data is normally distributed.”

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

This question tests your understanding of hypothesis testing errors.

How to Answer

Define both types of errors and provide examples to illustrate the differences.

Example

“A Type I error occurs when we reject a true null hypothesis, often referred to as a false positive. Conversely, a Type II error happens when we fail to reject a false null hypothesis, known as a false negative. For instance, in a medical test, a Type I error might indicate a patient has a disease when they do not, while a Type II error would suggest they are healthy when they actually have the disease.”

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

This question evaluates your understanding of experimental design and analysis.

How to Answer

Explain the concept of A/B testing and its application in decision-making processes.

Example

“A/B testing is used to compare two versions of a variable to determine which one performs better. By randomly assigning subjects to either group A or group B, we can analyze the results to make data-driven decisions, such as optimizing a website layout or marketing strategy based on user engagement metrics.”

Data Visualization

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

This question assesses your familiarity with visualization tools and their applications.

How to Answer

Discuss the tools you are proficient in and the reasons for your preferences based on their features and usability.

Example

“I primarily use Tableau and Power BI for data visualization due to their user-friendly interfaces and powerful capabilities for creating interactive dashboards. They allow me to present complex data insights clearly and effectively to stakeholders.”

2. How do you choose the right type of visualization for your data?

This question evaluates your understanding of effective communication through visualization.

How to Answer

Explain the factors you consider when selecting visualization types, such as data type, audience, and the message you want to convey.

Example

“I choose visualization types based on the data characteristics and the story I want to tell. For instance, I use bar charts for categorical comparisons, line graphs for trends over time, and scatter plots for relationships between two continuous variables. Understanding the audience is also crucial to ensure the visualization is accessible and informative.”

3. Can you describe a time when your visualization helped drive a business decision?

This question allows you to showcase your practical experience in data visualization.

How to Answer

Share a specific example where your visualization had a significant impact on decision-making.

Example

“In a project analyzing customer feedback, I created a dashboard that visualized sentiment trends over time. This visualization highlighted a significant drop in satisfaction after a product change, prompting the team to investigate and ultimately revert the change, which improved customer satisfaction scores.”

4. How do you ensure your visualizations are accessible to all stakeholders?

This question assesses your awareness of inclusivity in data presentation.

How to Answer

Discuss strategies you use to make visualizations accessible, such as color choices, labeling, and providing context.

Example

“I ensure accessibility by using color palettes that are colorblind-friendly and providing clear labels and legends. Additionally, I include descriptive titles and annotations to provide context, making it easier for all stakeholders to understand the insights being presented.”

5. What is the importance of storytelling in data visualization?

This question evaluates your understanding of the role of narrative in data presentation.

How to Answer

Explain how storytelling enhances the effectiveness of data visualization in conveying insights.

Example

“Storytelling in data visualization is crucial because it helps to contextualize the data and engage the audience. By framing the data within a narrative, I can guide stakeholders through the insights, making it easier for them to grasp the implications and take action based on the findings.”