Cardinal Health Data Scientist Interview Questions + Guide in 2025

Cardinal Health is a global healthcare services and products company that leverages technology to enhance patient outcomes and streamline operations in the healthcare industry.

As a Data Scientist within Cardinal Health's Artificial Intelligence Center of Excellence (AI CoE), you will play a critical role in leveraging data science and AI to develop innovative solutions that address complex healthcare challenges. This role involves collaborating with business stakeholders to translate their needs into actionable data-driven strategies, while also building and deploying machine learning models and generative AI solutions. Responsibilities include developing user-friendly applications, constructing robust APIs, maintaining end-to-end machine learning pipelines, and creating compelling data visualizations that communicate insights effectively.

To excel in this role, you should possess strong programming skills in Python and experience with relevant libraries (such as Scikit-learn and Pandas), expertise in cloud platforms (particularly Google Cloud Platform), and familiarity with machine learning operations (MLOps) and CI/CD practices. Additionally, a background in healthcare data and a solid understanding of machine learning fundamentals are essential.

This guide will help you prepare for a job interview by equipping you with insights into the expectations and experiences related to the Data Scientist role at Cardinal Health, allowing you to demonstrate your fit for the position effectively.

The interview process for a Data Scientist role at Cardinal Health is structured and thorough, designed to assess both technical and interpersonal skills. It typically consists of several key stages:

1. Initial Phone Screen

The process begins with a phone interview, usually lasting about 30 minutes. This initial conversation is typically conducted by a recruiter who will discuss your background, experience, and interest in the role. They will also provide insights into the company culture and the specifics of the Data Scientist position. This is an opportunity for you to express your motivations and clarify any questions you may have about the role.

2. Take-Home Assessment

Following the initial screen, candidates are often required to complete a take-home assessment. This assessment is designed to evaluate your technical skills, particularly in data manipulation, machine learning, and programming. While you may not need to complete every aspect of the assessment, it is crucial to demonstrate your thought process and problem-solving abilities. Be prepared to explain your approach and any limitations you encountered during the task.

3. Technical Interview (Based on Assessment)

Once the assessment is submitted, candidates typically participate in a technical interview that focuses on the solutions provided in the take-home assessment. This interview may involve a live coding session or a discussion of your approach to the problems presented. Interviewers will assess your understanding of data science concepts, programming skills, and your ability to articulate your thought process clearly.

4. Technical Interview (With the Team)

The next step usually involves a technical interview with members of the data science team. This round dives deeper into your technical expertise, including your knowledge of machine learning algorithms, data visualization, and programming languages such as Python and SQL. Expect questions that require you to solve problems on the spot, as well as discussions about your previous projects and experiences in the healthcare domain.

5. Final HR Interview

The final stage of the interview process is typically an HR interview. This conversation focuses on your fit within the company culture, your career aspirations, and how you align with Cardinal Health's values. HR may also discuss compensation, benefits, and any logistical details related to the role. This is your chance to ask about the team dynamics and the company's approach to professional development.

As you prepare for these interviews, it's essential to be ready for a variety of questions that will test your technical knowledge and problem-solving skills.

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

Understand the Interview Process

Familiarize yourself with Cardinal Health's structured interview process, which typically includes a phone call, a take-home assessment, and multiple technical interviews. Knowing this will help you prepare adequately for each stage. During the take-home assessment, focus on demonstrating your thought process and problem-solving skills, even if you can't complete every aspect. Be ready to discuss your approach and any challenges you faced.

Showcase Your Technical Skills

As a Data Scientist, you will be expected to demonstrate proficiency in key technical areas such as Python, SQL, and machine learning frameworks. Brush up on your knowledge of libraries like Pandas, Scikit-learn, and TensorFlow. Be prepared to answer questions about data preprocessing, model training, and evaluation techniques. Additionally, practice coding problems that involve data manipulation and analysis, as these are likely to come up during technical interviews.

Highlight Your Experience with Generative AI

Given the emphasis on Generative AI and machine learning in the role, be prepared to discuss your experience with these technologies. Familiarize yourself with concepts like large language models (LLMs), retrieval-augmented generation (RAG), and embedding techniques. If you have worked on projects involving these technologies, be ready to share specific examples and the impact they had on your previous work.

Emphasize Collaboration and Communication

Cardinal Health values collaboration and teamwork. Be prepared to discuss how you have worked with cross-functional teams in the past, particularly in translating business needs into data-driven solutions. Highlight your ability to communicate complex technical concepts to non-technical stakeholders, as this will be crucial in your role.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities, adaptability, and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you demonstrated leadership, overcame obstacles, or contributed to a team project, especially in a healthcare context.

Stay Current with Industry Trends

Cardinal Health is committed to innovation and staying ahead in the healthcare industry. Show your enthusiasm for continuous learning by discussing recent trends in AI and data science. Mention any relevant conferences, workshops, or online courses you have attended, and how they have influenced your work.

Align with Company Culture

Cardinal Health promotes a culture of collaboration, continuous learning, and diversity. Research the company’s values and mission, and think about how your personal values align with theirs. Be prepared to discuss how you can contribute to fostering an inclusive and innovative environment.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers. This not only shows your interest in the role but also helps you assess if the company is the right fit for you. Consider asking about the team dynamics, ongoing projects, or how success is measured in the role.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Cardinal Health. The interview process will likely focus on your technical skills in machine learning, data engineering, and software development, as well as your ability to collaborate with cross-functional teams and communicate complex concepts effectively. Be prepared to demonstrate your knowledge of healthcare data and your experience with generative AI technologies.

Machine Learning

1. Can you describe your experience with developing and deploying machine learning models?

This question aims to assess your practical experience in building machine learning solutions.

How to Answer

Discuss specific projects where you designed, trained, and optimized models. Highlight the types of models you worked with and the outcomes achieved.

Example

“I developed a churn prediction model for a healthcare application using logistic regression. After training the model on historical patient data, I optimized it using hyperparameter tuning, which improved accuracy by 15%. The model was deployed in a production environment, allowing the business to proactively engage at-risk patients.”

2. What techniques do you use for model evaluation and optimization?

This question evaluates your understanding of model performance metrics and optimization strategies.

How to Answer

Mention specific metrics you use (e.g., accuracy, precision, recall) and techniques like cross-validation or grid search for optimization.

Example

“I typically use accuracy and F1-score for classification models. I implement k-fold cross-validation to ensure the model generalizes well. For optimization, I utilize grid search to find the best hyperparameters, which has significantly improved model performance in my previous projects.”

3. How do you handle imbalanced datasets in your machine learning projects?

This question tests your knowledge of data preprocessing techniques.

How to Answer

Discuss methods like resampling, using different evaluation metrics, or applying algorithms that handle imbalance.

Example

“When faced with imbalanced datasets, I often use techniques like SMOTE for oversampling the minority class. Additionally, I focus on metrics like the area under the ROC curve to better evaluate model performance in these scenarios.”

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

This question assesses your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples of each.

Example

“Supervised learning involves training a model on labeled data, such as predicting patient outcomes based on historical data. In contrast, unsupervised learning deals with unlabeled data, like clustering patients based on their treatment responses without predefined categories.”

Generative AI

1. What experience do you have with Generative AI technologies, particularly large language models?

This question evaluates your familiarity with cutting-edge AI technologies.

How to Answer

Discuss specific projects or applications where you utilized generative AI, including any frameworks or libraries used.

Example

“I worked on a project that involved implementing a large language model to generate patient summaries from clinical notes. I used Hugging Face’s Transformers library to fine-tune the model, which improved the accuracy of the summaries significantly.”

2. How do you approach prompt engineering for generative models?

This question assesses your understanding of how to effectively interact with generative AI systems.

How to Answer

Explain your strategies for crafting effective prompts and how they impact the model's output.

Example

“I focus on clarity and specificity when crafting prompts. For instance, when generating patient summaries, I provide context about the patient’s history and specify the desired format, which helps the model produce more relevant and structured outputs.”

Data Engineering

1. Describe your experience with building end-to-end machine learning pipelines.

This question evaluates your ability to integrate various components of a machine learning project.

How to Answer

Outline the steps you take in building a pipeline, from data ingestion to model deployment.

Example

“I built an end-to-end pipeline for a predictive analytics project that included data ingestion from various sources, feature engineering using Pandas, model training with Scikit-learn, and deployment using Docker containers. This pipeline was automated using Apache Airflow, ensuring smooth operations.”

2. What tools and technologies do you prefer for data ingestion and processing?

This question assesses your familiarity with data engineering tools.

How to Answer

Mention specific tools you have used and why you prefer them.

Example

“I prefer using Apache Kafka for real-time data ingestion due to its scalability and reliability. For processing, I often use Apache Spark, as it allows for efficient handling of large datasets and supports both batch and stream processing.”

Collaboration and Communication

1. How do you ensure effective communication with non-technical stakeholders?

This question evaluates your ability to bridge the gap between technical and non-technical teams.

How to Answer

Discuss your strategies for simplifying complex concepts and ensuring alignment with business goals.

Example

“I focus on using visual aids and clear language when presenting technical concepts to non-technical stakeholders. For instance, I created dashboards that visually represented model performance metrics, making it easier for the team to understand the impact of our work on business objectives.”

2. Can you provide an example of a successful collaboration with cross-functional teams?

This question assesses your teamwork skills and ability to work in a collaborative environment.

How to Answer

Share a specific instance where you worked with different teams to achieve a common goal.

Example

“I collaborated with data engineers and product managers to develop a new feature for our healthcare application. By aligning our goals and maintaining open communication, we successfully integrated a predictive model that improved user engagement by 20%.”