EY Data Scientist Interview Questions + Guide in 2025

EY is a global leader in assurance, tax, transaction, and consulting services, committed to building a better working world by helping clients leverage innovative solutions and technologies.

The Data Scientist role at EY involves collaborating with a diverse range of clients to harness the power of data and advanced analytics to drive business outcomes. Key responsibilities include developing and executing comprehensive AI strategies, managing Model Operations (ModelOps) architecture, and ensuring adherence to ethical AI practices. The ideal candidate will possess a strong foundation in statistical analysis, machine learning, and data engineering, as well as a deep understanding of AI technologies and their ethical implications. They should demonstrate robust communication skills to convey complex technical concepts to non-technical stakeholders and exhibit leadership qualities to manage multidisciplinary teams effectively.

At EY, you'll be expected to continuously adapt to the evolving landscape of AI and data science while fostering relationships with clients and delivering high-quality services. This guide will help you prepare for your interview by providing insight into the skills, attributes, and experiences that are highly valued in this role.

The interview process for a Data Scientist role at EY is structured and thorough, designed to assess both technical and interpersonal skills. Candidates can expect multiple rounds of interviews, each focusing on different aspects of their qualifications and fit for the role.

1. Initial Screening

The first step typically involves an initial screening, which may be conducted via a phone call or a video interview. During this stage, a recruiter will discuss your background, experience, and motivations for applying to EY. This is also an opportunity for you to ask questions about the company culture and the specifics of the role. Candidates should be prepared to articulate their career goals and how they align with EY's mission and values.

2. Technical Assessment

Following the initial screening, candidates usually undergo a technical assessment. This may include a combination of coding challenges, statistical problem-solving, and discussions around machine learning concepts. Candidates might be asked to demonstrate their proficiency in programming languages such as Python or R, as well as their understanding of data manipulation and analysis techniques. Expect questions that require you to explain your thought process and approach to solving complex data-related problems.

3. Behavioral Interviews

The next phase often consists of one or more behavioral interviews. These interviews are typically conducted by team members or managers and focus on assessing your soft skills, such as teamwork, communication, and leadership abilities. Candidates should be ready to provide examples from their past experiences that demonstrate their problem-solving skills, ability to work in diverse teams, and how they handle challenges in a professional setting.

4. Case Study or Practical Exercise

In some instances, candidates may be required to complete a case study or practical exercise. This could involve analyzing a dataset, building a predictive model, or presenting findings based on a given scenario. This step is crucial as it allows candidates to showcase their analytical skills and ability to apply theoretical knowledge to real-world situations.

5. Final Interview

The final interview is often with senior leadership or a partner at EY. This round may include a mix of technical and behavioral questions, but it will also focus on your fit within the company culture and your long-term career aspirations. Candidates should be prepared to discuss their vision for their role and how they can contribute to EY's goals, particularly in the context of responsible AI and ethical data practices.

Throughout the interview process, candidates should be mindful of EY's emphasis on high ethical standards and integrity. Demonstrating a commitment to these values will be key to making a positive impression.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and past experiences.

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

Prepare for the Video Interview

The first round of interviews at EY often involves prerecorded video questions. Take the time to practice your responses to common questions, especially those about your personal and educational goals. Be clear about why you want to work at EY and how the role aligns with your aspirations. Use this opportunity to showcase your personality and enthusiasm for the position, as this format allows you to present yourself authentically.

Brush Up on Technical Knowledge

Expect technical questions that cover statistics, machine learning techniques, and programming languages like Python and SQL. Review key concepts such as regression models, data mining algorithms, and the differences between data structures (e.g., lists vs. tuples). Be prepared to discuss your past projects and how you applied these techniques in real-world scenarios. Practicing coding problems and statistical questions will help you feel more confident during the technical interviews.

Understand the Company Culture

EY values high ethical standards and integrity. Familiarize yourself with their commitment to responsible AI and ethical practices in data science. Be ready to discuss how you can contribute to these values in your role. Demonstrating an understanding of the company's culture and how you align with it can set you apart from other candidates.

Communicate Clearly and Confidently

During the interviews, especially when discussing complex technical concepts, aim to communicate clearly and concisely. Practice explaining your thought process and solutions in a way that is accessible to non-technical stakeholders. This skill is crucial, as you will often need to bridge the gap between technical and non-technical team members.

Showcase Leadership and Team Collaboration

As a Data Scientist at EY, you will likely be leading teams and collaborating with various stakeholders. Be prepared to share examples of how you have successfully led projects or worked within diverse teams. Highlight your ability to foster relationships and deliver quality client services, as these are key attributes that EY looks for in candidates.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities, adaptability, and teamwork. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you faced challenges and how you overcame them, particularly in a team setting.

Follow Up and Stay Engaged

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity to interview. This not only shows your professionalism but also keeps you on the interviewers' radar. If you don’t hear back within the expected timeframe, don’t hesitate to follow up politely to inquire about your application status.

By preparing thoroughly and demonstrating your alignment with EY's values and expectations, you can position yourself as a strong candidate for the Data Scientist role. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Data Scientist role at EY. The interview process will likely assess your technical skills, understanding of data science principles, and your ability to apply these concepts in a business context. Be prepared to discuss your past experiences, technical knowledge, and how you can contribute to EY's mission of leveraging AI responsibly.

Experience and Background

1. Can you describe a project where you implemented a machine learning model? What challenges did you face?

This question aims to assess your practical experience with machine learning and your problem-solving skills.

How to Answer

Discuss a specific project, focusing on the model you implemented, the data you used, and the challenges you encountered. Highlight how you overcame these challenges and the impact of your work.

Example

“In my last role, I developed a predictive model to forecast customer churn. One challenge was dealing with missing data, which I addressed by implementing imputation techniques. The model ultimately improved our retention strategy, leading to a 15% reduction in churn.”

2. What is your understanding of Responsible AI, and why is it important?

This question evaluates your knowledge of ethical AI practices, which is crucial for the role.

How to Answer

Explain the concept of Responsible AI, emphasizing fairness, accountability, and transparency. Discuss its importance in building trust with clients and ensuring compliance with regulations.

Example

“Responsible AI refers to the ethical use of AI technologies, ensuring that models are fair, transparent, and accountable. It’s vital because it helps prevent biases in decision-making and builds trust with clients, aligning with regulatory requirements.”

Machine Learning

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 techniques such as cross-validation, regularization, and pruning. Explain how you would apply these methods to improve model performance.

Example

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

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

This question assesses your foundational knowledge of machine learning paradigms.

How to Answer

Define both terms clearly and provide examples of each. Highlight the types of problems each approach is best suited for.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices. In contrast, unsupervised learning deals with unlabeled data, identifying patterns or groupings, like customer segmentation.”

Statistics and Probability

5. What statistical methods do you use to validate your models?

This question evaluates your statistical knowledge and its application in model validation.

How to Answer

Discuss methods such as hypothesis testing, confidence intervals, and A/B testing. Explain how these methods help in assessing model performance.

Example

“I often use A/B testing to compare model performance against a baseline. Additionally, I apply hypothesis testing to determine if the improvements are statistically significant, ensuring that the results are reliable.”

6. Can you explain the concept of p-values and their significance in hypothesis testing?

This question tests your understanding of statistical significance.

How to Answer

Define p-values and explain their role in hypothesis testing, including what they indicate about the null hypothesis.

Example

“A p-value measures the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

Data Engineering

7. What experience do you have with data preprocessing and cleaning?

This question assesses your data handling skills, which are crucial for any data science role.

How to Answer

Discuss specific techniques you’ve used for data cleaning, such as handling missing values, outlier detection, and normalization.

Example

“I have extensive experience in data preprocessing, including handling missing values through imputation and removing outliers using Z-scores. I also normalize data to ensure that features contribute equally to model training.”

8. How do you ensure data quality in your projects?

This question evaluates your approach to maintaining high data standards.

How to Answer

Discuss methods for data validation, monitoring, and cleaning processes you implement to ensure data integrity.

Example

“I ensure data quality by implementing validation checks at the data ingestion stage and regularly monitoring data pipelines for anomalies. Additionally, I conduct periodic audits to identify and rectify any data quality issues.”

Behavioral Questions

9. Describe a time when you had to work with a difficult stakeholder. How did you handle it?

This question assesses your interpersonal skills and ability to manage relationships.

How to Answer

Provide a specific example, focusing on the situation, your approach to communication, and the outcome.

Example

“I once worked with a stakeholder who was skeptical about the value of our data analysis. I scheduled a meeting to understand their concerns and presented data-driven insights that directly addressed their objectives. This open communication helped build trust and led to a successful collaboration.”

10. Why do you want to work at EY, and how do you see yourself contributing?

This question gauges your motivation for applying and your understanding of the company’s values.

How to Answer

Discuss your alignment with EY’s mission and values, and how your skills and experiences can contribute to their goals.

Example

“I admire EY’s commitment to ethical practices in AI and data science. I believe my experience in developing responsible AI solutions aligns perfectly with your mission. I’m excited about the opportunity to help clients navigate the complexities of AI while ensuring compliance and ethical standards.”