Marsh Machine Learning Engineer Interview Questions + Guide in 2025

Marsh is a global leader in insurance broking and risk management, providing innovative solutions to clients across various industries.

As a Machine Learning Engineer at Marsh, you will be responsible for designing, developing, and implementing machine learning models and algorithms that enhance business processes and support data-driven decision-making. Key responsibilities include collaborating with cross-functional teams to identify opportunities for leveraging machine learning, building predictive models, and deploying scalable solutions that meet the unique needs of our clients. A strong foundation in programming languages such as Python or Java, proficiency in machine learning frameworks, and experience with cloud platforms are essential. Additionally, you should possess excellent problem-solving skills, a collaborative mindset, and the ability to communicate complex concepts clearly to non-technical stakeholders.

This guide will equip you with insights and tailored questions that reflect the expectations and culture at Marsh, allowing you to confidently navigate the interview process and showcase your fit for the role.

The interview process for a Machine Learning Engineer at Marsh is designed to assess both technical skills and cultural fit within the organization. It typically unfolds in several structured stages, ensuring a comprehensive evaluation of candidates.

1. Initial Phone Screening

The process begins with a phone screening conducted by an HR representative. This initial conversation is generally straightforward, focusing on your background, motivations for applying to Marsh, and basic qualifications for the role. Expect questions that gauge your understanding of the company and your ability to articulate your experiences and skills.

2. Video Interview

Following the phone screening, candidates may be invited to participate in a recorded video interview. This format can be somewhat intimidating, as it often includes a series of pre-set questions that require thoughtful responses. The questions are tailored to the specific role and may involve problem-solving scenarios relevant to machine learning and data analysis.

3. HR Interview

Successful candidates from the video interview will typically move on to a more relaxed HR interview. This session is often conducted by the department manager and focuses on behavioral questions that explore your past experiences, challenges you've faced, and how you handle various situations. The aim is to assess your interpersonal skills and alignment with Marsh's values.

4. Technical Interview

The next step usually involves a technical interview, which may be conducted in-person or via video call. This interview is more in-depth and focuses on your technical expertise in machine learning, programming languages, and relevant tools. Be prepared to discuss your previous projects, methodologies, and any specific technologies you have worked with, such as Java, Python, or machine learning frameworks.

5. Final Interview

In some cases, candidates may have a final interview with higher management or a panel of peers. This stage often includes a mix of technical and personal questions, allowing interviewers to assess your fit within the team and the broader company culture. Expect to discuss your long-term career goals and how they align with the opportunities at Marsh.

Throughout the process, candidates are encouraged to demonstrate their problem-solving abilities, communication skills, and enthusiasm for the role.

As you prepare for your interview, consider the types of questions that may arise during these stages.

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

Understand Marsh's Values and Culture

Before your interview, take the time to familiarize yourself with Marsh's core values and company culture. This will not only help you answer questions like "Why Marsh?" but also allow you to align your responses with what the company stands for. Marsh values communication, collaboration, and innovation, so be prepared to discuss how you embody these traits in your work.

Prepare for Behavioral Questions

Expect a range of behavioral questions that assess your problem-solving abilities and how you handle difficult situations. Use the STAR (Situation, Task, Action, Result) method to structure your responses. For instance, when asked about a challenging project, clearly outline the context, your specific role, the actions you took, and the outcome. This approach will demonstrate your analytical thinking and ability to reflect on past experiences.

Showcase Your Technical Skills

As a Machine Learning Engineer, you will likely face technical questions related to algorithms, data structures, and programming languages such as Java or Python. Brush up on your knowledge of machine learning frameworks and be ready to discuss your previous projects in detail. Be prepared to explain your thought process and the rationale behind your technical decisions.

Emphasize Communication Skills

Given the emphasis on communication at Marsh, be ready to discuss how you effectively collaborate with team members and stakeholders. You might be asked about your approach to managing project communication or how you handle disagreements within a team. Highlight your ability to convey complex technical concepts to non-technical audiences, as this is crucial in a collaborative environment.

Be Ready for Scenario-Based Questions

You may encounter scenario-based questions that assess your problem-solving skills in real-world situations. For example, you might be asked how you would convince a skeptical user about the benefits of a machine learning solution. Think through your responses in advance, focusing on how you would approach the situation, the steps you would take, and the expected outcomes.

Maintain a Relaxed Yet Professional Demeanor

Interviews at Marsh are described as friendly and comfortable, so aim to create a relaxed atmosphere while maintaining professionalism. Engage with your interviewers, ask thoughtful questions, and show genuine interest in the role and the company. This will help you build rapport and leave a positive impression.

Follow Up Thoughtfully

After your interview, consider sending a thank-you email to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your enthusiasm for the role and briefly mention any key points from the interview that you found particularly engaging. This not only shows your professionalism but also keeps you top of mind for the hiring team.

By following these tips, you can position yourself as a strong candidate for the Machine Learning Engineer role at Marsh. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Marsh. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the company. Be prepared to discuss your experiences, technical knowledge, and how you approach challenges in machine learning and data analysis.

Technical Skills

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

Understanding the fundamental concepts of machine learning is crucial. Be clear and concise in your explanation, providing examples of each type.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, highlighting the key differences in their applications and outcomes.

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 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 in real-world scenarios.

How to Answer

Outline the project’s objectives, your role, the challenges encountered, and how you overcame them.

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to balance the dataset and improve model performance, which ultimately led to a 15% increase in retention rates.”

3. How do you handle overfitting in a machine learning model?

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

How to Answer

Discuss various strategies to prevent overfitting, such as regularization, cross-validation, and pruning.

Example

“To handle overfitting, I use techniques like L1 and L2 regularization to penalize large coefficients. Additionally, I implement cross-validation to ensure the model generalizes well to unseen data, and I may simplify the model by reducing its complexity.”

4. What metrics do you use to evaluate the performance of a machine learning model?

This question gauges your knowledge of model evaluation and the importance of selecting appropriate metrics.

How to Answer

Mention specific metrics relevant to the type of problem you are solving, such as accuracy, precision, recall, F1 score, or AUC-ROC.

Example

“I typically use accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall to ensure the model performs well on minority classes. For binary classification, I also look at the AUC-ROC curve to assess the trade-off between true positive and false positive rates.”

Behavioral Questions

1. Why do you want to work for Marsh?

This question assesses your motivation and alignment with the company’s values and mission.

How to Answer

Express your interest in the company’s culture, projects, or values that resonate with you.

Example

“I admire Marsh’s commitment to innovation and its focus on leveraging data to drive business decisions. I believe my skills in machine learning can contribute to enhancing your data-driven strategies, and I’m excited about the opportunity to work in such a collaborative environment.”

2. Tell me about a time you faced a difficult situation at work and how you overcame it.

This question evaluates your problem-solving and interpersonal skills.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response.

Example

“In a previous role, our team faced a tight deadline for a project. I organized daily stand-up meetings to track progress and address any blockers. By fostering open communication, we completed the project on time and received positive feedback from stakeholders.”

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

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, such as using frameworks or tools to manage tasks effectively.

Example

“I prioritize tasks based on urgency and impact. I use tools like Trello to visualize my workload and set deadlines. I also communicate with my team to ensure alignment on priorities, which helps in managing expectations and delivering quality work.”

4. Describe a time when you had to convince a non-technical stakeholder about a technical solution.

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

How to Answer

Share an example where you successfully communicated complex ideas in a simplified manner.

Example

“I once presented a machine learning solution to a marketing team. I focused on the business impact rather than the technical details, using visuals to illustrate how the model could improve customer targeting. This approach helped them understand the value, and they were on board with the implementation.”

Problem-Solving Scenarios

1. How would you approach a project that is not progressing as planned?

This question assesses your problem-solving and adaptability skills.

How to Answer

Discuss your approach to identifying issues and implementing solutions.

Example

“I would first analyze the project’s current status to identify bottlenecks. Then, I would engage with the team to gather insights and brainstorm solutions. If necessary, I’d adjust the project plan and reallocate resources to ensure we meet our objectives.”

2. What would you do if you encountered a significant bug in your model just before deployment?

This question tests your ability to handle pressure and troubleshoot effectively.

How to Answer

Explain your troubleshooting process and how you would communicate with stakeholders.

Example

“I would first replicate the issue to understand its root cause. Then, I’d prioritize fixing the bug and run tests to ensure the solution works. I would keep stakeholders informed about the situation and the steps being taken to resolve it, ensuring transparency throughout the process.”

3. How do you stay updated with the latest trends and technologies in machine learning?

This question evaluates your commitment to continuous learning and professional development.

How to Answer

Mention specific resources, communities, or practices you engage with to stay informed.

Example

“I regularly read research papers and follow industry blogs like Towards Data Science. I also participate in online courses and attend conferences to network with other professionals and learn about emerging technologies in machine learning.”

4. Describe a time when you had to work with a team to achieve a common goal.

This question assesses your teamwork and collaboration skills.

How to Answer

Use the STAR method to describe your role in the team and the outcome.

Example

“In a recent project, I collaborated with data engineers and product managers to develop a recommendation system. I facilitated communication between the teams, ensuring everyone was aligned on objectives. Our combined efforts led to a successful launch that increased user engagement by 20%.”