Divisions Maintenance Group Data Scientist Interview Questions + Guide in 2025

Divisions Maintenance Group (DMG) is a leader in facility maintenance services, delivering innovative solutions to retail chains and distribution centers across the United States.

The Data Scientist role at DMG is essential for building and scaling their data science capabilities, particularly within their Marketplace Health and Pricing teams. This position involves translating complex business problems into advanced analytics solutions, leveraging a diverse set of large and unstructured data to derive meaningful insights. Key responsibilities include writing production-level code for robust analytics products, collaborating with stakeholders, and applying advanced analytical techniques such as predictive modeling and machine learning. A successful candidate should possess strong problem-solving skills, a mastery of statistical methods, and experience with data wrangling and software engineering best practices, particularly within a cloud-native environment.

This guide will help you prepare for your interview by providing insights into the expectations of the role and the skills you need to demonstrate to stand out as a candidate at DMG.

The interview process for a Data Scientist at Divisions Maintenance Group is structured yet straightforward, designed to assess both technical and interpersonal skills essential for the role.

1. Initial Phone Screen

The process typically begins with a 30-minute phone interview conducted by a recruiter. This initial conversation focuses on understanding your background, skills, and motivations for applying. The recruiter will also provide insights into the company culture and the specifics of the role, ensuring you have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screen, candidates usually undergo a technical assessment. This may involve a series of multiple-choice questions that evaluate your aptitude in logical reasoning, programming, and statistical concepts. You may also be asked to solve coding problems, which could include writing code snippets or addressing specific algorithmic challenges relevant to data science.

3. Shadowing Experience

A unique aspect of the interview process is a shadowing session, where candidates spend approximately 30 minutes observing a current employee in the Data Scientist role. This experience allows you to gain firsthand insight into the day-to-day responsibilities and challenges faced in the position, while also providing an opportunity to ask questions.

4. In-Person Interviews

The final stage typically consists of in-person interviews with hiring managers and team members. These interviews delve deeper into your technical expertise, including discussions on your previous projects, experience with statistical modeling, and familiarity with programming languages such as Python and SQL. Behavioral questions are also common, focusing on how you handle complex business problems and collaborate with stakeholders.

5. Final Evaluation

After the in-person interviews, candidates may undergo a final evaluation, which could include discussions about your fit within the team and the company culture. This stage is crucial as it assesses not only your technical skills but also your ability to communicate complex concepts to both technical and non-technical audiences.

As you prepare for your interview, consider the types of questions that may arise during this process, particularly those that assess your technical skills and problem-solving abilities.

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

Understand the Company Culture

Divisions Maintenance Group emphasizes a strong, inclusive culture and values work-life balance. However, be prepared for a fast-paced environment that may require you to be in the office regularly. Familiarize yourself with the company's mission of providing "Uninterrupted Peace of Mind" and think about how your skills and experiences align with this goal. This understanding will help you articulate why you are a good fit for the company.

Prepare for a Structured Interview Process

The interview process typically includes multiple rounds, starting with a phone screen followed by in-person interviews. Expect a mix of technical and behavioral questions. Be ready to discuss your past experiences in detail, particularly how they relate to the role of a Data Scientist. Prepare to showcase your problem-solving skills and your ability to communicate complex concepts to both technical and non-technical audiences.

Brush Up on Technical Skills

Given the emphasis on statistics, algorithms, and programming, ensure you are well-versed in Python and SQL. Be prepared to solve coding problems and discuss your experience with statistical modeling techniques, such as hypothesis testing and machine learning. Familiarize yourself with data wrangling techniques and be ready to discuss how you have applied these in previous roles.

Showcase Your Problem-Solving Abilities

The role requires structured problem-solving skills and the ability to translate complex business problems into data science solutions. Prepare examples from your past work where you successfully identified a problem, developed a solution, and communicated your findings effectively. This will demonstrate your analytical thinking and your ability to work collaboratively with stakeholders.

Communicate Clearly and Confidently

During the interview, focus on clear and concise communication. Practice explaining your past projects and technical concepts in a way that is accessible to a variety of audiences. This skill is crucial, as you will need to synthesize and communicate findings to stakeholders with varying levels of technical expertise.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your fit within the team and company culture. Reflect on your past experiences, particularly those that demonstrate your ability to work under pressure, manage stress, and adapt to a fast-paced environment. Use the STAR (Situation, Task, Action, Result) method to structure your responses.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers. This not only shows your interest in the role but also helps you gauge if the company is the right fit for you. Inquire about the team dynamics, the types of projects you would be working on, and how success is measured within the Data Science team.

Follow Up Professionally

After the interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the role and briefly mention how your skills align with the company's goals. This small gesture can leave a positive impression and keep you top of mind as they make their decision.

By following these tips, you will be well-prepared to navigate the interview process at Divisions Maintenance Group and demonstrate your qualifications for the Data Scientist role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Divisions Maintenance Group. The interview process will likely focus on your technical skills, problem-solving abilities, and experience in translating complex business problems into data-driven solutions. Be prepared to discuss your past projects, methodologies, and how you can contribute to the company's goals.

Machine Learning

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

Understanding the distinction between these two types of learning is fundamental in data science.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each method is best suited for.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like customer segmentation in marketing data.”

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

Outline the project, your role, the techniques used, and the challenges encountered. Emphasize how you overcame these challenges.

Example

“I worked on a project to predict equipment failures in a manufacturing setting. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to balance the dataset, which improved our model's accuracy significantly.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using multiple metrics. For classification tasks, I focus on precision and recall to understand the trade-offs between false positives and false negatives. For regression tasks, I often use RMSE to assess prediction accuracy.”

4. What techniques do you use for feature selection?

Feature selection is crucial for improving model performance and interpretability.

How to Answer

Mention techniques like recursive feature elimination, LASSO regression, and tree-based methods, and explain their importance.

Example

“I use recursive feature elimination to iteratively remove features and assess model performance. Additionally, I apply LASSO regression to penalize less important features, which helps in reducing overfitting and improving model interpretability.”

Statistics & Probability

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

Understanding p-values is essential for statistical analysis.

How to Answer

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

Example

“A 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 our findings are 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.

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.”

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 strategies such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use mean imputation for small amounts of missing data or consider more sophisticated methods like KNN imputation for larger gaps.”

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

This question tests your understanding of statistical errors.

How to Answer

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

Example

“A Type I error occurs when we incorrectly 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. Understanding these errors is vital for interpreting the results of hypothesis tests.”

Programming & Technical Skills

1. What is your experience with Python for data analysis?

This question assesses your programming skills and familiarity with data analysis libraries.

How to Answer

Discuss your experience with Python libraries such as Pandas, NumPy, and Scikit-learn, and how you have used them in past projects.

Example

“I have extensive experience using Python for data analysis, particularly with Pandas for data manipulation and Scikit-learn for building machine learning models. For instance, I used Pandas to clean and preprocess a large dataset before applying various machine learning algorithms.”

2. How do you ensure the quality of your code?

This question evaluates your coding practices and attention to detail.

How to Answer

Mention practices such as code reviews, unit testing, and adherence to coding standards.

Example

“I ensure code quality by following best practices such as writing unit tests to validate functionality and conducting code reviews with peers. I also use version control systems like Git to track changes and maintain a clean codebase.”

3. Describe your experience with SQL and how you use it in your projects.

SQL is a critical skill for data manipulation and retrieval.

How to Answer

Discuss your experience with SQL queries, database management, and how you have used SQL in data analysis.

Example

“I have used SQL extensively for data extraction and manipulation. In my previous role, I wrote complex queries to join multiple tables and aggregate data for analysis, which helped in generating insights for business decisions.”

4. Can you explain how you would design an API for a data science application?

This question assesses your understanding of API design and integration.

How to Answer

Discuss the key components of API design, including endpoints, data formats, and authentication.

Example

“I would design an API with RESTful principles, defining clear endpoints for data retrieval and submission. I would use JSON for data interchange and implement OAuth for secure authentication, ensuring that the API is scalable and easy to integrate with other applications.”