McKesson Machine Learning Engineer Interview Questions + Guide in 2025

McKesson is a leading healthcare company that focuses on delivering insights, products, and services that make quality care more accessible and affordable.

As a Machine Learning Engineer at McKesson, you will play a vital role in the AI/ML Engineering Services team, leading the design, development, and deployment of advanced machine learning models, especially in the context of enhancing cybersecurity measures. Your responsibilities will include collaborating with subject matter experts and engineers to identify complex challenges, integrating machine learning models into existing systems, and mentoring junior engineers to foster a culture of continuous learning. A strong proficiency in Python and familiarity with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn are essential, alongside a deep understanding of cybersecurity principles. You should be an excellent communicator, capable of presenting findings and strategies effectively to senior management and stakeholders.

This guide will equip you with insights and tailored questions to prepare for your interview, helping you stand out as a candidate who aligns with McKesson's commitment to innovation and impact in healthcare.

The interview process for a Machine Learning Engineer at McKesson is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of the candidate's qualifications and experience.

1. Initial Screening

The process begins with an initial phone screening conducted by a recruiter. This call usually lasts around 20-30 minutes and focuses on your background, experience, and motivation for applying to McKesson. The recruiter will also provide insights into the company culture and the specifics of the role. Be prepared to discuss your resume in detail and answer questions about your interest in the position.

2. Technical Interview

Following the initial screening, candidates typically undergo a technical interview. This may be conducted via video conferencing and will focus on your technical skills, particularly in machine learning and programming. Expect questions related to your experience with machine learning frameworks such as TensorFlow or PyTorch, as well as your proficiency in Python. You may also be asked to solve coding problems or discuss past projects that demonstrate your technical capabilities.

3. Behavioral Interview

After the technical assessment, candidates often participate in a behavioral interview. This stage is crucial as it helps the interviewers gauge your soft skills, problem-solving abilities, and how you handle various workplace scenarios. Questions may revolve around your leadership experiences, teamwork, and how you approach challenges. The STAR (Situation, Task, Action, Result) method is recommended for structuring your responses.

4. Panel Interview

The final stage usually involves a panel interview with multiple team members, including senior engineers and management. This session can be more extensive, lasting several hours, and may include a mix of technical and behavioral questions. You may also be asked to present your past work or a case study relevant to the role. This is an opportunity to showcase your communication skills and ability to articulate complex concepts clearly.

5. Offer and Negotiation

If you successfully navigate the interview stages, you may receive a job offer. The offer process at McKesson is generally straightforward, with discussions around compensation and benefits. Be prepared to negotiate based on your experience and the market standards.

As you prepare for your interviews, consider the types of questions that may arise in each stage, particularly those that focus on your technical expertise and past experiences.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at McKesson. The interview process will likely focus on your technical expertise in machine learning, your experience with cybersecurity applications, and your ability to lead and mentor a team. Be prepared to discuss your past projects, problem-solving skills, and how you can contribute to the company's mission.

Machine Learning

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

This question aims to assess your hands-on experience with machine learning projects, particularly in a production environment.

How to Answer

Discuss specific projects where you led the development and deployment of machine learning models, emphasizing the challenges faced and how you overcame them.

Example

“In my previous role, I developed a machine learning model to predict patient readmission rates. I led the project from data collection to deployment, ensuring the model was integrated into the existing healthcare system. This involved collaborating with cross-functional teams to address data quality issues and iterating on the model based on feedback from stakeholders.”

2. What machine learning frameworks are you most comfortable with, and why?

This question evaluates your familiarity with industry-standard tools and frameworks.

How to Answer

Mention the frameworks you have used, your level of expertise with each, and why you prefer them for specific tasks.

Example

“I have extensive experience with TensorFlow and PyTorch. I prefer TensorFlow for its robust production capabilities and scalability, especially when deploying models in cloud environments. However, I find PyTorch more intuitive for research and experimentation due to its dynamic computation graph.”

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

This question tests your understanding of model evaluation metrics and techniques.

How to Answer

Discuss the metrics you use to evaluate model performance, such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain how you choose the appropriate metric based on the problem.

Example

“I evaluate model performance using a combination of accuracy and F1 score, especially in imbalanced datasets. For instance, in a fraud detection model, I prioritize precision to minimize false positives, ensuring that legitimate transactions are not flagged incorrectly.”

4. Describe a challenging machine learning problem you faced and how you solved it.

This question assesses your problem-solving skills and ability to handle complex situations.

How to Answer

Provide a specific example of a challenging problem, the steps you took to address it, and the outcome.

Example

“I once worked on a project where the data was highly noisy and incomplete. I implemented a data preprocessing pipeline that included outlier detection and imputation techniques. This significantly improved the model's performance, leading to a 20% increase in accuracy.”

5. How do you stay updated with the latest trends and advancements in machine learning?

This question gauges your commitment to continuous learning in a rapidly evolving field.

How to Answer

Mention specific resources, such as journals, conferences, online courses, or communities you engage with to stay informed.

Example

“I regularly read research papers from arXiv and attend conferences like NeurIPS and ICML. I also participate in online courses on platforms like Coursera to deepen my understanding of emerging techniques and tools.”

Cybersecurity

1. What experience do you have with machine learning applications in cybersecurity?

This question focuses on your relevant experience in applying machine learning to cybersecurity challenges.

How to Answer

Discuss specific projects or experiences where you applied machine learning to detect or mitigate cybersecurity threats.

Example

“I developed a machine learning model to detect phishing emails by analyzing email content and metadata. The model achieved a 95% detection rate, significantly reducing the number of successful phishing attempts within the organization.”

2. How do you approach integrating machine learning models into existing cybersecurity systems?

This question assesses your understanding of system integration and collaboration with other teams.

How to Answer

Explain your approach to integration, including collaboration with cybersecurity experts and ensuring compatibility with existing systems.

Example

“I start by collaborating with cybersecurity SMEs to understand the existing systems and their requirements. I then ensure that the machine learning model can be seamlessly integrated by using APIs and maintaining thorough documentation for future reference.”

3. Can you explain a time when you had to address a conflict of interest in a project?

This question evaluates your ethical considerations and conflict resolution skills.

How to Answer

Provide a specific example of a conflict of interest you encountered and how you resolved it while maintaining integrity.

Example

“In a previous project, there was a conflict regarding data usage between departments. I facilitated a meeting to discuss the implications and worked towards a compromise that allowed both teams to use the data while adhering to compliance regulations.”

4. What strategies do you use to mentor junior engineers in machine learning?

This question assesses your leadership and mentoring abilities.

How to Answer

Discuss your mentoring philosophy and specific strategies you employ to support junior engineers.

Example

“I believe in hands-on mentoring, so I often pair program with junior engineers on projects. I also encourage them to take ownership of smaller tasks and provide regular feedback, fostering a culture of continuous learning and improvement.”

5. How do you handle tight deadlines when working on machine learning projects?

This question evaluates your time management and prioritization skills.

How to Answer

Explain your approach to managing deadlines, including prioritization and communication with stakeholders.

Example

“When faced with tight deadlines, I prioritize tasks based on their impact and feasibility. I communicate regularly with stakeholders to manage expectations and ensure that we focus on delivering the most critical features first.”