EY Machine Learning Engineer Interview Questions + Guide in 2025

EY is a global professional services firm that focuses on building a better working world through innovative solutions and high ethical standards.

As a Machine Learning Engineer at EY, you will be an integral part of the Artificial Intelligence and Data team, collaborating closely with clients to apply cutting-edge technologies and techniques to solve complex challenges. This role involves designing and building scalable solutions that integrate and derive insights from diverse data sources across a wide technology landscape. You'll need to demonstrate strong technical skills, especially in Python and machine learning frameworks, while also showcasing your ability to effectively communicate findings and recommendations to clients and team members alike.

Key responsibilities include developing end-to-end machine learning workflows, maintaining and optimizing models, and ensuring that solutions meet evolving business needs. Success in this role requires a passion for continuous learning, an agile mindset, and the ability to work collaboratively within a dynamic, interdisciplinary team. Ideal candidates will possess strong mathematical skills, experience with MLOps practices, and the willingness to engage with clients in a fast-paced environment.

This guide will help you prepare for an interview by providing insights into the skills and experiences EY values, as well as the types of questions you may encounter during the process.

The interview process for a Machine Learning Engineer at EY is structured and thorough, reflecting the company's commitment to finding the right fit for their dynamic teams. Typically, candidates can expect a multi-stage process that assesses both technical and interpersonal skills.

1. Application and Initial Screening

The process begins with an online application, where candidates submit their resumes and cover letters. Following this, there is an initial screening call with a recruiter. This conversation is generally informal and focuses on understanding the candidate's background, motivations, and fit for EY's culture. The recruiter may also discuss the role's expectations and the overall interview process.

2. Technical Assessment

After the initial screening, candidates often undergo a technical assessment. This may include an online coding test or a take-home assignment that evaluates proficiency in relevant programming languages, particularly Python, and familiarity with machine learning concepts. Candidates might be asked to solve problems related to data manipulation, algorithm implementation, or model evaluation.

3. Technical Interviews

Successful candidates from the technical assessment will proceed to one or more technical interviews. These interviews typically involve discussions with team members or technical leads and focus on machine learning principles, data science methodologies, and practical applications. Candidates should be prepared to answer questions about their previous projects, demonstrate their understanding of machine learning frameworks, and possibly solve coding problems in real-time.

4. Behavioral Interviews

In addition to technical skills, EY places a strong emphasis on cultural fit and interpersonal skills. Candidates will likely participate in behavioral interviews where they will be asked to provide examples of past experiences, particularly in teamwork, conflict resolution, and stakeholder management. These interviews aim to assess how candidates align with EY's values and their ability to thrive in a collaborative environment.

5. Final Interview

The final stage often involves a discussion with senior management or a partner. This interview may cover strategic thinking, leadership potential, and the candidate's vision for their role within the company. It is also an opportunity for candidates to ask questions about the team dynamics, project expectations, and career development opportunities at EY.

Throughout the process, candidates should be prepared for a variety of questions that assess both their technical expertise and their ability to communicate effectively with clients and team members.

Next, let's delve into the specific interview questions that candidates have encountered during their interviews at EY.

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

Understand the Company Culture

EY values high ethical standards, integrity, and inclusivity. Familiarize yourself with their core values and how they translate into everyday work. Be prepared to discuss how your personal values align with those of the company. This will not only demonstrate your fit for the role but also your commitment to contributing positively to the company culture.

Prepare for Technical Depth

Given the technical nature of the Machine Learning Engineer role, ensure you are well-versed in Python, popular ML libraries (like scikit-learn and PyTorch), and MLOps practices. Be ready to discuss your experience with generative AI models and frameworks, as well as your understanding of machine learning workflows. Expect to solve case studies or technical problems during the interview, so practice articulating your thought process clearly.

Showcase Your Problem-Solving Skills

Interviews at EY often involve case studies and situational questions. Prepare to discuss specific challenges you've faced in previous projects, how you approached them, and the outcomes. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your analytical and problem-solving skills.

Emphasize Client Interaction Experience

Since the role involves heavy client interaction, be prepared to discuss your experience working with clients. Highlight instances where you successfully managed stakeholder expectations or navigated differing priorities. This will demonstrate your ability to communicate effectively and build relationships, which are crucial in a consulting environment.

Be Ready for Behavioral Questions

Expect a mix of technical and behavioral questions. Prepare for questions about your leadership style, how you handle pressure, and your approach to teamwork. Reflect on your past experiences and be ready to share examples that showcase your adaptability, collaboration, and growth mindset.

Practice Your Presentation Skills

Given the emphasis on client-facing roles, you may be asked to present your ideas or solutions during the interview. Practice presenting complex technical concepts in a clear and engaging manner. This will not only help you during the interview but also prepare you for the client interactions that are a key part of the role.

Follow Up and Seek Feedback

The interview process at EY can be lengthy, and feedback may not be immediate. Be proactive in following up with your interviewers or HR for updates. This shows your enthusiasm for the role and your commitment to the process. Additionally, if you receive feedback, use it constructively to improve your future interviews.

Stay Curious and Open-Minded

EY is looking for candidates who are curious and purpose-driven. During your interview, express your eagerness to learn and adapt to new technologies and methodologies. Share examples of how you have pursued continuous learning in your career, whether through formal education, self-study, or professional development opportunities.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically proficient but also a great cultural fit for EY. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Machine Learning Engineer position at EY. The interview process will likely focus on your technical expertise in machine learning, your ability to work with clients, and your problem-solving skills in a fast-paced environment. Be prepared to discuss your past experiences, technical knowledge, and how you approach challenges in the field.

Technical Skills

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 linear regression for predicting house prices. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior using K-means.”

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

This question assesses your understanding of model performance and generalization.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Mention how you would evaluate model performance using metrics.

Example

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

3. Describe your experience with MLOps and CI/CD in machine learning projects.

This question evaluates your practical experience with deploying machine learning models.

How to Answer

Explain your familiarity with MLOps practices, tools, and how you have implemented CI/CD pipelines in your projects.

Example

“I have implemented MLOps practices using MLFlow for tracking experiments and model versions. I also set up CI/CD pipelines using Azure DevOps to automate testing and deployment, ensuring that our models are continuously integrated and delivered efficiently.”

4. What is your approach to feature selection in a dataset?

This question tests your knowledge of data preprocessing and model optimization.

How to Answer

Discuss methods for feature selection, such as correlation analysis, recursive feature elimination, or using algorithms like LASSO.

Example

“I typically start with correlation analysis to identify features that have a strong relationship with the target variable. I also use recursive feature elimination to iteratively remove less important features, which helps improve model performance and reduce complexity.”

5. Can you explain how you would evaluate the performance of a classification model?

This question assesses your understanding of model evaluation metrics.

How to Answer

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

Example

“I evaluate classification models using accuracy for a general overview, but I also consider precision and recall, especially in imbalanced datasets. The F1-score provides a balance between precision and recall, while ROC-AUC helps assess the model's ability to distinguish between classes.”

Behavioral Questions

1. How do you handle stakeholders with differing priorities?

This question evaluates your interpersonal skills and ability to manage expectations.

How to Answer

Discuss your approach to communication, understanding stakeholder needs, and finding common ground.

Example

“I prioritize open communication by scheduling regular check-ins with stakeholders to understand their needs. When priorities differ, I facilitate discussions to align our goals and ensure that everyone feels heard, which helps in finding a compromise that satisfies all parties.”

2. Describe a challenging project you worked on and how you overcame obstacles.

This question assesses your problem-solving skills and resilience.

How to Answer

Share a specific example, focusing on the challenges faced, your actions, and the outcomes.

Example

“In a recent project, we faced data quality issues that hindered our model's performance. I initiated a data cleaning process, collaborating with the data engineering team to identify and rectify inconsistencies. This effort improved our model's accuracy significantly and met the project deadline.”

3. How do you ensure effective communication within your team?

This question evaluates your teamwork and leadership skills.

How to Answer

Discuss your strategies for fostering communication, such as regular meetings, collaborative tools, and feedback mechanisms.

Example

“I promote effective communication by implementing daily stand-ups and using collaboration tools like Slack and Trello. I encourage team members to share updates and challenges, fostering an environment where feedback is welcomed and acted upon.”

4. How do you stay updated with the latest trends in machine learning?

This question assesses your commitment to continuous learning.

How to Answer

Mention resources you use, such as online courses, conferences, and research papers.

Example

“I stay updated by following leading machine learning blogs, attending conferences like NeurIPS, and taking online courses on platforms like Coursera. I also participate in local meetups to network with other professionals and share insights.”

5. Can you provide an example of how you’ve contributed to a team’s success?

This question evaluates your teamwork and collaborative spirit.

How to Answer

Share a specific instance where your contributions positively impacted the team or project.

Example

“In a recent project, I took the initiative to mentor junior team members on machine learning concepts. By organizing knowledge-sharing sessions, I helped elevate the team's overall skill level, which contributed to the successful delivery of our project ahead of schedule.”