Cardinal Health Machine Learning Engineer Interview Questions + Guide in 2025

Cardinal Health is a global corporation that plays a pivotal role in the healthcare industry, delivering innovative solutions that enhance patient care and streamline operations across the spectrum of healthcare services.

As a Machine Learning Engineer at Cardinal Health’s Artificial Intelligence Center of Excellence (AI CoE), you will engage in developing and deploying advanced AI solutions that significantly impact patient outcomes and operational efficiency. Your key responsibilities will include designing and implementing machine learning models, creating robust data pipelines, building RESTful APIs to integrate AI services, and developing user interfaces for applications utilizing machine learning. The role demands a strong foundation in machine learning algorithms and frameworks, proficiency in programming languages such as Python, and experience with cloud platforms like Google Cloud Platform (GCP). Additionally, a collaborative mindset is essential, as you will be working with diverse teams to bridge technical capabilities with business requirements.

A great fit for this position is someone who not only possesses technical expertise but also embodies Cardinal Health’s commitment to innovation, teamwork, and improving healthcare delivery. The ideal candidate will be passionate about exploring emerging technologies and continuously learning in a fast-paced environment.

This guide will help you prepare thoroughly for your interview by providing insights into the expectations and nuances of the role, equipping you with the knowledge to articulate your skills effectively and demonstrate your alignment with Cardinal Health's mission and values.

The interview process for a Machine Learning Engineer at Cardinal Health is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the dynamic environment of the Artificial Intelligence Center of Excellence (AI CoE). The process typically unfolds as follows:

1. Initial Phone Screen

The first step in the interview process is a phone screen with a recruiter. This conversation usually lasts around 20-30 minutes and serves to gauge your interest in the role, discuss your background, and assess your fit within the company culture. The recruiter will likely ask about your experience with machine learning, programming languages, and any relevant projects you've worked on.

2. Technical Assessment

Following the initial screen, candidates may be required to complete a technical assessment. This could be a take-home assignment or an online coding test that evaluates your proficiency in machine learning concepts, programming (particularly in Python), and familiarity with relevant libraries such as TensorFlow or PyTorch. The assessment is designed to test your ability to apply theoretical knowledge to practical problems.

3. Technical Interviews

Candidates who successfully pass the technical assessment will move on to a series of technical interviews. Typically, there are two or more rounds, each lasting about 30-45 minutes. These interviews are conducted by team members, including data scientists and engineers, and focus on your technical skills, problem-solving abilities, and understanding of machine learning algorithms. Expect questions that require you to demonstrate your knowledge of model development, data processing, and API integration.

4. Behavioral Interviews

In addition to technical interviews, candidates will also participate in behavioral interviews. These sessions are designed to assess your soft skills, teamwork, and cultural fit within the organization. Interviewers will ask scenario-based questions to understand how you handle challenges, collaborate with others, and communicate with stakeholders. This part of the process is crucial, as Cardinal Health values effective communication and collaboration in its teams.

5. Final Interview with Management

The final step often involves a meeting with management or senior team members. This interview may include a presentation of your previous work or a discussion of your approach to specific projects. It’s an opportunity for you to showcase your expertise and how it aligns with the company’s goals. Be prepared to discuss your long-term career aspirations and how you envision contributing to the AI CoE.

Throughout the process, candidates should expect timely communication from the HR team, although some candidates have noted delays in feedback. It's essential to remain proactive and follow up if you haven't heard back after your interviews.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages.

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

Understand the Company Culture

Cardinal Health emphasizes collaboration, innovation, and a commitment to improving healthcare outcomes. Familiarize yourself with their mission and values, particularly how they leverage data and AI to enhance patient care. Be prepared to discuss how your personal values align with the company's goals and how you can contribute to their mission.

Prepare for Technical Depth

Given the technical nature of the Machine Learning Engineer role, ensure you are well-versed in machine learning algorithms, Python programming, and relevant libraries such as TensorFlow and PyTorch. Be ready to discuss your previous projects in detail, including the challenges you faced and how you overcame them. Expect to answer technical questions that may require you to demonstrate your problem-solving skills in real-time.

Anticipate Behavioral Questions

Interviews at Cardinal Health often include behavioral questions that assess your teamwork and communication skills. Prepare examples from your past experiences that showcase your ability to collaborate with cross-functional teams, handle conflicts, and adapt to changing requirements. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Engage with the Interviewers

During the interview, actively engage with your interviewers. Ask insightful questions about the team dynamics, ongoing projects, and the technologies they are currently exploring. This not only demonstrates your interest in the role but also helps you gauge if the company culture and team environment are a good fit for you.

Be Ready for a Multi-Round Process

The interview process may involve multiple rounds, including technical assessments and discussions with various team members. Stay organized and be prepared to discuss your experiences in different contexts. If you encounter any technical assessments, approach them with a problem-solving mindset, and don’t hesitate to explain your thought process.

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 the team's needs. This not only shows professionalism but also keeps you on their radar as they make their decision.

Stay Informed on Emerging Technologies

Given the focus on Generative AI and machine learning at Cardinal Health, make sure to stay updated on the latest trends and advancements in these areas. Being knowledgeable about current technologies and methodologies will not only help you in the interview but also demonstrate your commitment to continuous learning and innovation.

By following these tips, you can present yourself as a well-prepared and enthusiastic candidate who is ready to contribute to Cardinal Health's mission of improving healthcare through innovative AI solutions. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Cardinal Health. The interview process will likely focus on your technical skills in machine learning, software engineering, and your ability to collaborate with business stakeholders. Be prepared to discuss your experience with AI technologies, data processing, and your approach to problem-solving in a healthcare context.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key differences, such as the presence of labeled data in supervised learning versus the absence in unsupervised learning. Provide examples of algorithms used in each.

Example

“Supervised learning involves training a model on a labeled dataset, where the outcome is known, such as using regression for predicting house prices. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers 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.

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them. Highlight any specific techniques or tools used.

Example

“I worked on a project to predict patient readmission rates. One challenge was dealing with missing data. I implemented imputation techniques and feature engineering to enhance model performance, which ultimately improved our prediction accuracy by 15%.”

3. What techniques do you use for feature selection?

Feature selection is critical for model performance, and interviewers want to know your approach.

How to Answer

Discuss various techniques such as recursive feature elimination, LASSO regression, or tree-based methods. Mention how you decide which features to keep.

Example

“I often use recursive feature elimination combined with cross-validation to select features. This method helps in identifying the most significant predictors while avoiding overfitting, ensuring that the model generalizes well to unseen data.”

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

Understanding model evaluation metrics is essential for any machine learning engineer.

How to Answer

Mention various metrics like accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall. For instance, in a healthcare application predicting disease presence, I focus on recall to minimize false negatives.”

5. Can you explain what overfitting is and how to prevent it?

Overfitting is a common issue in machine learning, and interviewers want to know your strategies to mitigate it.

How to Answer

Define overfitting and discuss techniques such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like L2 regularization and cross-validation to ensure the model performs well on unseen data.”

Software Engineering

1. What is your experience with RESTful APIs?

APIs are crucial for integrating machine learning models into applications.

How to Answer

Discuss your experience designing and implementing RESTful APIs, including any frameworks or tools you’ve used.

Example

“I have designed RESTful APIs using Flask to serve machine learning models. This involved creating endpoints for model predictions and ensuring efficient data handling between the front end and back end.”

2. How do you ensure code quality and maintainability in your projects?

This question assesses your software engineering practices.

How to Answer

Talk about practices like code reviews, unit testing, and adherence to coding standards.

Example

“I ensure code quality by implementing unit tests and conducting regular code reviews with my team. I also follow PEP 8 guidelines for Python to maintain readability and consistency across the codebase.”

3. Describe your experience with cloud platforms for deploying machine learning models.

Cloud deployment is often a key part of machine learning engineering roles.

How to Answer

Mention specific cloud platforms you’ve used, the services you leveraged, and any challenges faced.

Example

“I have deployed machine learning models on Google Cloud Platform using services like AI Platform for model training and Cloud Functions for serving predictions. This setup allowed for scalable and efficient model deployment.”

4. What tools do you use for version control and collaboration?

Version control is essential for team projects.

How to Answer

Discuss your experience with tools like Git and platforms like GitHub or GitLab.

Example

“I use Git for version control, managing branches for features and bug fixes. I collaborate with my team on GitHub, utilizing pull requests for code reviews and ensuring that our codebase remains stable.”

5. How do you approach debugging a machine learning model?

Debugging is a critical skill for any engineer.

How to Answer

Explain your systematic approach to identifying and resolving issues in models.

Example

“When debugging a model, I start by checking the data pipeline for inconsistencies. I then analyze model predictions against expected outcomes, using techniques like confusion matrices to identify where the model is failing.”

Collaboration and Communication

1. How do you communicate complex technical concepts to non-technical stakeholders?

Effective communication is key in a collaborative environment.

How to Answer

Discuss your strategies for simplifying complex ideas and ensuring understanding.

Example

“I focus on using analogies and visual aids to explain complex concepts. For instance, when discussing model performance, I might use a simple graph to illustrate accuracy versus recall, making it easier for stakeholders to grasp the implications.”

2. Describe a time you had to work with a difficult team member. How did you handle it?

This question assesses your interpersonal skills and conflict resolution abilities.

How to Answer

Share a specific example, focusing on your approach to resolving the conflict and maintaining professionalism.

Example

“I once worked with a team member who was resistant to feedback. I scheduled a one-on-one meeting to discuss our project goals and listened to their concerns. By fostering open communication, we were able to align our efforts and improve collaboration.”

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

Time management is crucial in a fast-paced environment.

How to Answer

Discuss your methods for prioritizing tasks, such as using project management tools or frameworks.

Example

“I use 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.”

4. Can you give an example of how you collaborated with business stakeholders to define project requirements?

Collaboration with stakeholders is essential for successful project outcomes.

How to Answer

Share a specific instance where you worked with stakeholders to gather requirements and translate them into actionable tasks.

Example

“In a recent project, I held workshops with business stakeholders to understand their needs for a predictive analytics tool. By facilitating discussions and gathering feedback, I was able to define clear project requirements that aligned with their goals.”

5. What strategies do you use to stay updated on emerging technologies in AI and machine learning?

Continuous learning is vital in the rapidly evolving field of AI.

How to Answer

Discuss your methods for staying informed, such as attending conferences, following industry publications, or participating in online courses.

Example

“I regularly attend AI and machine learning conferences and follow leading researchers on social media. I also participate in online courses to deepen my understanding of new technologies, ensuring that I stay current in this fast-paced field.”