CDK Global Data Scientist Interview Questions + Guide in 2025

CDK Global is a leading provider of cloud-based software solutions for the automotive industry, focusing on enhancing dealership operations and customer experiences.

As a Data Scientist at CDK Global, you will play a pivotal role in developing predictive models that directly impact the company’s software products. Your primary responsibilities will include analyzing vast amounts of clean data to create models that predict customer behaviors, such as the likelihood of a vehicle purchase based on their online interactions. Collaboration will be key in this role, as you will work alongside engineers, product managers, and UX designers to deploy microservices that support user interfaces, ensuring rapid response times for critical predictions.

To excel in this position, you should have a solid foundation in applied data science, with at least two years of experience in a software or customer-focused technology environment. Proficiency in Python and SQL is essential, as you'll be working with complex data blending and model development. Being an effective communicator will also be critical, as you will need to explain sophisticated analytical concepts in straightforward terms. Additionally, familiarity with business metrics such as revenue and market share will enhance your contributions, as you will be expected to lead discussions on best practices in data science and AI/ML methodologies. Experience in the automotive sector or knowledge of related technologies will be a significant advantage.

This guide aims to equip you with insights and strategies tailored to the Data Scientist role at CDK Global, helping you to stand out during the interview and demonstrate a strong alignment with the company’s mission and values.

The interview process for a Data Scientist role at CDK Global is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the collaborative and innovative environment of the company. The process typically consists of several key stages:

1. Initial Contact

The process begins with an initial contact from a recruiter, which may take place via phone or video call. During this conversation, the recruiter will provide an overview of the role and the company, while also gathering information about your background, skills, and motivations. This is an opportunity for you to express your interest in the position and ask any preliminary questions about the company culture and expectations.

2. Technical Assessment

Following the initial contact, candidates usually undergo a technical assessment. This may involve a coding challenge or a take-home assignment that tests your proficiency in Python and SQL, as well as your ability to solve data-related problems. The assessment is designed to evaluate your technical skills in a practical context, focusing on your ability to manipulate data, develop predictive models, and write efficient queries.

3. Onsite Interviews

The onsite interview stage typically consists of multiple rounds, often involving five or more interviewers. Each interviewer may focus on different aspects of your expertise, including technical skills, problem-solving abilities, and cultural fit. Expect questions related to your resume, past projects, and specific technical challenges you have faced. You may also be asked to demonstrate your SQL skills through live coding exercises or whiteboard sessions.

4. Behavioral Interviews

In addition to technical assessments, candidates will participate in behavioral interviews. These interviews aim to gauge your soft skills, such as communication, teamwork, and leadership abilities. Interviewers will be interested in how you approach collaboration with cross-functional teams, your storytelling capabilities in presenting complex data insights, and your understanding of business acumen.

5. Final Interview

The final interview may involve discussions with senior leadership or team members from different departments. This stage is often more conversational and focuses on your long-term career goals, alignment with CDK Global's values, and your vision for contributing to the team. It’s a chance for you to ask deeper questions about the company’s direction and how you can play a role in its success.

As you prepare for the interview process, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and collaborative experiences.

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

Understand the Role and Its Impact

As a Data Scientist at CDK Global, your work will directly influence the customer experience in the automotive industry. Familiarize yourself with how predictive modeling can enhance dealership operations and customer interactions. Be prepared to discuss how your previous experiences align with the responsibilities of developing models that predict consumer behavior and improve business outcomes.

Prepare for Technical Questions

Given the emphasis on SQL and Python in the role, ensure you are well-versed in both. Review complex SQL queries, including joins, subqueries, and window functions. Brush up on Python, focusing on both functional programming and modeling techniques. You may be asked to solve a programming problem or write SQL queries during the interview, so practice coding challenges that reflect real-world scenarios you might encounter at CDK.

Showcase Your Storytelling Skills

The ability to communicate complex analytical concepts clearly is crucial. Prepare to explain your past projects in a way that highlights your thought process, the challenges you faced, and the impact of your work. Use storytelling techniques to make your experiences relatable and engaging, demonstrating how you can translate data insights into actionable business strategies.

Emphasize Collaboration and Leadership

CDK Global values teamwork and collaboration across various departments. Be ready to discuss how you have worked with cross-functional teams in the past, particularly with engineers, product managers, and UX designers. Highlight any leadership experiences where you educated others on data science best practices or AI/ML methods, showcasing your ability to influence and guide your peers.

Familiarize Yourself with the Company Culture

CDK Global promotes an inclusive and diverse work environment. Reflect on how your values align with the company’s commitment to diversity and inclusion. Be prepared to discuss how you can contribute to a positive team culture and support the company’s mission of creating meaningful connections with customers and communities.

Leverage Your Passion for Automotive

If you have any experience or interest in the automotive industry, make sure to bring it up during the interview. This can set you apart from other candidates and demonstrate your enthusiasm for the role. If you don’t have direct experience, consider discussing your interest in cars and how that passion can translate into understanding customer needs in the automotive space.

Prepare Questions for Your Interviewers

Engage your interviewers by preparing thoughtful questions about the team dynamics, ongoing projects, and the company’s future direction. This not only shows your interest in the role but also helps you assess if CDK Global is the right fit for you. Ask about the tools and technologies the team uses, as well as opportunities for professional development and growth within the company.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Scientist role at CDK Global. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at CDK Global. The interview process will likely focus on your technical skills, problem-solving abilities, and your capacity to communicate complex concepts clearly. Be prepared to discuss your past experiences, particularly those that demonstrate your expertise in data science, machine learning, and your ability to work collaboratively with cross-functional teams.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial for this role, as you will be developing predictive models.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each approach 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 or groupings, like customer segmentation based on purchasing behavior.”

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 in real-world scenarios.

How to Answer

Outline the project, your role, the challenges encountered, and how you overcame them. Emphasize the impact of your work.

Example

“I worked on a project to predict customer churn for an e-commerce platform. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to balance the dataset and improved the model's accuracy by 15%, which helped the company retain more customers.”

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 how well the model predicts continuous outcomes.”

4. What techniques do you use to prevent overfitting in your models?

This question gauges your knowledge of model generalization.

How to Answer

Mention techniques like cross-validation, regularization, and pruning, and explain how they help in preventing overfitting.

Example

“To prevent overfitting, I use cross-validation to ensure the model performs well on unseen data. Additionally, I apply regularization techniques like L1 and L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

5. Can you explain what a confusion matrix is and how you would use it?

This question assesses your understanding of model evaluation in classification tasks.

How to Answer

Define a confusion matrix and explain how it provides insights into the performance of a classification model.

Example

“A confusion matrix is a table that summarizes the performance of a classification model by showing true positives, true negatives, false positives, and false negatives. It helps in calculating metrics like accuracy, precision, and recall, allowing for a deeper understanding of where the model is making errors.”

Statistics & Probability

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

This question tests your foundational knowledge in statistics.

How to Answer

Explain the theorem and its significance in statistical inference.

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.”

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

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, 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 imputation techniques like mean or median substitution, or if the missing data is substantial, I may consider using algorithms that can handle missing values directly.”

3. Can you explain the concept of p-value?

This question assesses your understanding of hypothesis testing.

How to Answer

Define p-value and its role in hypothesis testing, including its implications for statistical significance.

Example

“A p-value indicates 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.”

4. What is the difference between Type I and Type II errors?

This question tests your understanding of error types in hypothesis testing.

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, essentially a false positive. Conversely, a Type II error happens when we fail to reject a false null hypothesis, which is a false negative. Understanding these errors is crucial for interpreting the results of hypothesis tests accurately.”

5. How would you explain the concept of statistical power?

This question evaluates your grasp of statistical testing concepts.

How to Answer

Discuss what statistical power is and its importance in hypothesis testing.

Example

“Statistical power is the probability of correctly rejecting a false null hypothesis. It is influenced by sample size, effect size, and significance level. High power is essential to ensure that we can detect true effects when they exist, minimizing the risk of Type II errors.”