Interview questions for EY Data Science Consultant
Hi everyone, this topic is for sharing Preparation guidelines and interview experience for EY Data Science Consultant
The Data Science Consultant role at EY involves a multi-stage assessment and interview process, designed to evaluate both technical skills and business proficiency. Below is a summary of the process and key points from the interviews you provided:
Technical/ML/Data Science
Explain a machine learning model you’ve worked on and its impact.
How would you handle missing data in a dataset?
How do you evaluate the performance of a classification model?
Programming (Python/SQL)
Python knowledge-related questions (assess your Python fundamentals).
Write a SQL query to join two tables.
Case Study/Analytics
Solve a simple data science case study.
Solve a case study on optimizing a business process using data science.
HR/Personality/Behavioral
Tell me about yourself.
Why are you interested in this role at EY?
Describe a challenging project you worked on and how you handled it.
Describe a time you had to explain a technical concept to a non-technical stakeholder.
What are your salary expectations?
How do you handle feedback?
Where do you see yourself in 5 years?
General/Background (Profile Fit)
Walk me through your background or resume.
Interview Process Summary
Interview Rounds:
HR Phone Interview Initial screen covering background, motivation for EY, and high-level project experience.
Manager Interview (Video, ~45 minutes) General discussion about profile and background; candidates may need to steer the conversation toward role-relevant skills if the interviewer is not specific.
Technical Round – Python Questions focused on Python fundamentals and usage of common data science libraries.
Technical Round – ML/SQL Model explanation and business impact, handling missing data, evaluation metrics for classification, and SQL joins.
Case Study Round(s) One or two data science case studies assessing problem structuring, identification of key data points, analytical approach, assumptions, and business impact.
Final HR Discussion Compensation expectations, feedback style, and career goals.
Interview Preparation Tips:
Brush up on Python fundamentals and key libraries (Pandas, NumPy, scikit-learn).
Practice SQL basics, especially joins.
Review ML concepts and evaluation metrics (e.g., precision, recall, ROC/AUC) and data preprocessing techniques (e.g., handling missing data).
Practice end-to-end case studies; structure solutions clearly and link insights to business outcomes.
Prepare concise stories using the STAR method for behavioral questions and stakeholder communication.
Research EY’s work in data science to tailor your responses to their context and priorities.
Logistics and conduct: confirm the interviewer’s understanding of the role beforehand and be ready to guide the conversation if it goes off-track.
If you have attended the process from your campus, pls share your experiences here; Please follow [guidelines](https://discuss.boardinfinity.com/t/interview-transcript-guidelines/22428?u=abhay-gupta-ebaf4123)
Application Process: Received a call from the recruiter and scheduled a 45-minute video call with the Manager.
Interview Rounds:
Round 1 - Manager Interview:
Questions Asked: General questions about my profile and background. The manager seemed unclear about the position and did not ask any technical or role-specific questions.
Your Approach: I tried to steer the conversation toward my skills and experience relevant to data science, but the manager showed little interest.
Outcome: The round felt unproductive due to the lack of clarity and engagement from the manager.
Conclusion:
The experience was disappointing as the interviewer was unprepared and disinterested. For future candidates, I’d advise confirming the interviewer’s understanding of the role beforehand and being prepared to guide the conversation if needed.
Application Process: I applied via a referral and was interviewed in June 2024.
Interview Rounds:
Round 1 - Coding Test:
Questions Asked: The test had two sections:
Machine Learning MCQs: Questions covering fundamental ML concepts.
SQL + Python Programming: A practical coding question involving SQL and Python.
Your Approach: For the ML MCQs, I relied on my understanding of core ML algorithms and concepts. For the SQL and Python section, I focused on writing efficient queries and clean, functional code.
Outcome: I passed this round and moved forward in the process.
Preparation Tips:
Topics to Focus On:
Machine Learning (especially fundamentals and algorithms).
SQL (query writing and optimization).
Python (coding efficiency and problem-solving).
Resources: Practice coding problems on platforms like LeetCode and review ML concepts from standard textbooks or online courses.
Conclusion:
The interview process was smooth, and the questions were aligned with the role’s requirements. I found the SQL and Python section particularly challenging but manageable with proper preparation. For future candidates, I’d recommend brushing up on both theoretical ML and practical coding skills to ace this round.
Application Process: I applied through the company website and was interviewed in April 2024.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
Write a prompt to extract key Personal Identifiable Information (PII) given a resume.
Your Approach: I focused on creating a clear and concise prompt that would effectively identify and extract PII such as names, contact details, and addresses from a resume. I ensured the prompt was structured to handle variations in resume formats.
Outcome: The interviewer seemed satisfied with my approach and the clarity of the prompt.
Preparation Tips:
Have a strong grasp of basic Machine Learning (ML) and Deep Learning (DL) concepts before diving into more complex topics like Large Language Models (LLMs).
Practice creating prompts for data extraction tasks, as this is a common requirement in data science roles.
Conclusion:
The interview was a great learning experience, and I realized the importance of being able to articulate technical concepts clearly. For future candidates, I’d recommend focusing on both foundational knowledge and practical applications, especially in areas like prompt engineering for LLMs.
Application Process: [Application process details not provided]
Interview Rounds:
Round 1 - Coding Test:
Questions Asked:
Machine Learning concepts
Metrics (e.g., evaluation metrics for models)
Your Approach:
Reviewed key machine learning algorithms and their applications.
Practiced coding problems related to predictive modeling and data science.
Focused on understanding evaluation metrics like precision, recall, and F1-score.
Outcome: [Outcome not specified]
Preparation Tips:
Brush up on Python, especially libraries like Pandas, NumPy, and Scikit-learn.
Understand core machine learning concepts and how to apply them in real-world scenarios.
Practice explaining your thought process clearly during coding and problem-solving tasks.
Conclusion:
The interview process was focused on technical skills, particularly in machine learning and data science. Preparing thoroughly for coding and conceptual questions is key. If I could do anything differently, I would spend more time on practical applications of machine learning models to better showcase my skills during the interview.
Application Process: Applied through an online application process.
Interview Rounds:
Round 1 - Phone Interview (HR):
Questions Asked: General questions about my background, motivation for applying, and understanding of the role.
Your Approach: I focused on aligning my skills and experiences with the job requirements and emphasized my enthusiasm for data science and consulting.
Outcome: Passed to the next round.
Round 2 - Assessment Center:
Questions Asked: Included a group case study, a one-on-one case study with a manager, and online logic tests.
Your Approach: For the group case study, I actively participated and collaborated with others. For the one-on-one case, I structured my analysis clearly and communicated my thought process. The logic tests were timed, so I practiced similar tests beforehand.
Outcome: Successfully advanced to the final round.
Round 3 - Partner Interview:
Questions Asked: Discussed my previous experiences, problem-solving approach, and how I handle challenges. Also, some behavioral questions.
Your Approach: I used the STAR method for behavioral questions and linked my answers to the role’s demands.
Outcome: Received positive feedback and an offer.
Preparation Tips:
Practiced case studies and logic tests to improve speed and accuracy.
Reviewed common HR and behavioral questions to prepare for the phone and partner interviews.
Researched EY’s work in data science to tailor my answers.
Conclusion:
Overall, the process was thorough but well-structured. The group case study was challenging but a great learning experience. I could have prepared more for the logic tests to perform even better. My advice is to practice case studies and logic tests extensively and to clearly articulate your thought process during interviews.
Application Process: Approached by the company for the role.
Interview Rounds:
Round 1 - Case Study Round:
Questions Asked: Simple Data Science Case Study.
Your Approach: Analyzed the problem statement thoroughly, identified key data points, and proposed a structured solution using relevant data science techniques.
Your Approach: Demonstrated proficiency in Python by explaining concepts clearly and providing examples where necessary.
Outcome: Advanced to the next round.
Round 3 - Case Study Round:
Questions Asked: Data Science Case Study.
Your Approach: Applied advanced data science methodologies, validated assumptions, and presented a well-reasoned solution.
Outcome: Cleared the round and progressed further in the hiring process.
Preparation Tips:
Brush up on Python fundamentals, especially libraries like Pandas, NumPy, and Scikit-learn.
Practice solving case studies to improve problem-solving and analytical skills.
Be prepared to explain your thought process clearly during technical discussions.
Conclusion:
The interview process was structured and focused on assessing both technical and analytical skills. The case study rounds were particularly insightful, and I felt well-prepared for the technical questions. For future candidates, I recommend practicing real-world case studies and being confident in explaining your solutions.
Application Process: The process began with an HR call, where they scheduled a phone interview with a manager. If cleared, the next steps involved two to three technical interviews conducted on-site, usually by team members. After these rounds, the HR would get back with either an offer or rejection.
Interview Rounds:
Round 1 - HR Screening:
Questions Asked: General questions about my background, interest in the role, and availability.
Your Approach: I kept my responses concise and aligned them with the job description, emphasizing my enthusiasm for the role.
Outcome: Cleared this round and was scheduled for a phone interview with the manager.
Round 2 - Manager Phone Interview:
Questions Asked: More detailed questions about my experience, technical skills, and problem-solving approach. The manager also asked about my familiarity with data science tools and methodologies.
Your Approach: I provided specific examples from my past projects to demonstrate my skills and how they align with the role. I also asked clarifying questions to show my interest.
Outcome: Successfully moved to the next round of on-site interviews.
Round 3 - Technical Interview (On-Site):
Questions Asked: In-depth technical questions, including coding challenges, data analysis scenarios, and case studies. The team also asked about my experience with machine learning models and data visualization.
Your Approach: I tackled the coding challenges methodically, explaining my thought process. For case studies, I structured my answers clearly and linked them to real-world applications.
Outcome: Cleared this round and proceeded to the final technical interview.
Round 4 - Final Technical Interview (On-Site):
Questions Asked: Advanced technical questions, including system design for data pipelines, optimization techniques, and behavioral questions about teamwork and conflict resolution.
Your Approach: I focused on demonstrating my ability to design scalable solutions and collaborate effectively. I also highlighted my adaptability and problem-solving skills.
Outcome: Received positive feedback and was informed that HR would follow up.
Preparation Tips:
Brush up on core data science concepts, including statistics, machine learning, and programming (Python/R).
Practice coding challenges on platforms like LeetCode or HackerRank.
Prepare for behavioral questions using the STAR method.
Review case studies related to data science applications in consulting.
Conclusion:
The interview process was thorough but well-structured. I felt prepared for the technical rounds, but I could have spent more time refining my answers for behavioral questions. My advice for future candidates is to balance technical preparation with soft skills and to stay confident throughout the process.
Application Process: Approached by the company for the role.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
What is the role of beta value in Logistic regression?
What is bias-variance trade-off?
How did you decide on the list of variables that would be used in a model?
Your Approach: I focused on explaining the theoretical concepts clearly, providing examples where applicable. For the variable selection question, I discussed methods like feature importance, correlation analysis, and domain knowledge.
Outcome: Passed the round.
Round 2 - Case Study Round:
Questions Asked:
You are the data scientist of a digital store. You have to recommend top 10 products to a customer. What variables and techniques will you use to recommend the top 10 products?
Your Approach: I discussed variables like purchase history, browsing behavior, and customer demographics. For techniques, I mentioned collaborative filtering, content-based filtering, and hybrid recommendation systems.
Outcome: Successfully cleared the round.
Preparation Tips:
Be thorough with the projects and use cases you have previously worked on.
Understand the theoretical foundations of machine learning and data science concepts.
Practice case studies related to real-world business problems, especially in the domain of recommendation systems.
Conclusion:
The interview process was smooth, and the questions were aligned with the role’s requirements. I felt confident discussing my projects and theoretical knowledge. For future candidates, I recommend focusing on both technical depth and practical application of concepts. Good luck!
Application Process: Applied via the company website before September 2022.
Interview Rounds:
Round 1 - Resume Shortlist:
Questions Asked: Resume review for relevance and clarity.
Your Approach: Ensured my resume was concise and highlighted key skills and experiences.
Outcome: Successfully shortlisted for the next round.
Round 2 - HR Round:
Questions Asked: Questions about my current role and responsibilities.
Your Approach: Clearly explained my role and how it aligns with the position at EY.
Outcome: Advanced to the technical round.
Round 3 - Technical Round:
Questions Asked: Multiple questions related to Data Science coding tests.
Your Approach: Prepared by revising core Data Science concepts and practicing coding problems.
Outcome: Cleared the technical round.
Round 4 - Cultural Interview Round:
Questions Asked: Two rounds of tests to assess cultural and team fit.
Your Approach: Answered honestly and aligned my responses with EY’s values and work culture.
Outcome: Successfully cleared the cultural fit assessment.
Preparation Tips:
Focus on making your resume concise and relevant.
Brush up on Data Science concepts and coding skills for the technical round.
Research the company’s culture to align your responses during the cultural interview.
Conclusion:
Overall, the interview process was thorough but fair. Preparing my resume well and revising technical concepts helped me succeed. For future candidates, I’d recommend practicing coding problems and understanding the company’s values to excel in the cultural fit round.
Application Process: I applied for the position in October 2020. The entire interview process spanned over 4 rounds and took almost 5 months to complete.
Interview Rounds:
Round 1 - Technical Interview:
Questions Asked:
Explain all the technologies you used in your projects.
Why did you choose those specific technologies/algorithms?
Explain KDD and explain each step in detail.
A little bit of SQL and applied ML algorithms.
Your Approach: I focused on clearly articulating the rationale behind my technology choices and demonstrated my understanding of KDD (Knowledge Discovery in Databases) by breaking down each step. For SQL and ML algorithms, I provided practical examples from my experience.
Outcome: Successfully cleared this round.
Round 2 - HR Interview:
Questions Asked: Not specified.
Your Approach: I ensured my responses aligned with the company’s values and culture, emphasizing teamwork and problem-solving skills.
Outcome: Cleared this round.
Round 3 - Assignment:
Questions Asked: Not specified.
Your Approach: I approached the assignment methodically, ensuring clarity and thoroughness in my solutions.
Outcome: Cleared this round.
Round 4 - Final Interview:
Questions Asked: Not specified.
Your Approach: I maintained confidence and professionalism, reiterating my fit for the role and my enthusiasm for the opportunity.
Outcome: Successfully cleared the final round.
Preparation Tips:
Be thorough with your project details, including the technologies and algorithms used.
Understand the basics of KDD and be prepared to explain it in detail.
Brush up on SQL and applied ML algorithms, as they are likely to be tested.
The interviewers are professional yet friendly, so stay calm and composed.
Conclusion:
The interview process at EY was quite extensive but well-structured. The recruiters and interviewers were professional and approachable. While the process took almost 5 months, it was a great learning experience. My advice to future candidates is to be patient, prepare thoroughly, and stay confident throughout the process.
Application Process: I applied for the position in October 2020. The entire process took almost 5 months, but the recruiters and interviewers were very professional and friendly throughout.
Interview Rounds:
Round 1 - Technical Interview:
Questions Asked:
Explain all the technologies you used in your projects.
Why did you choose those specific technologies/algorithms?
Explain KDD and explain each step in detail.
A little bit of SQL and applied ML algorithms.
Your Approach: I focused on clearly articulating the rationale behind my technology choices and provided detailed explanations for each step of KDD. For SQL and ML algorithms, I ensured my answers were concise and practical.
Outcome: Passed this round.
Round 2 - HR Interview:
Questions Asked: [Not specified]
Your Approach: I maintained a professional yet friendly demeanor, aligning my responses with the company’s values and culture.
Outcome: Passed this round.
Round 3 - Assignment:
Questions Asked: [Not specified]
Your Approach: I tackled the assignment methodically, ensuring clarity and accuracy in my solutions.
Outcome: Passed this round.
Round 4 - Interview Questions:
Questions Asked: [Not specified]
Your Approach: I prepared thoroughly by revisiting key concepts and staying calm during the interview.
Outcome: Passed this round.
Preparation Tips:
Be ready to explain your project technologies and choices in detail.
Brush up on KDD, SQL, and applied ML algorithms.
Stay patient, as the process might take longer than expected.
Conclusion:
Overall, the interview experience with EY was positive. The recruiters and interviewers were professional and approachable. If I could do anything differently, I would perhaps prepare more thoroughly for the HR round to align even better with the company’s culture. For future candidates, my advice is to stay patient and ensure you understand the fundamentals of your projects and the technologies you’ve used.
Application Process: Applied through the company’s career portal. The HR team reached out to schedule an initial phone interview.
Interview Rounds:
Round 1 - HR Phone Interview:
Questions Asked:
Tell me about yourself.
Why are you interested in this role at EY?
Describe a challenging project you worked on and how you handled it.
Your Approach:
Prepared a concise introduction highlighting relevant experience.
Researched EY’s work in data science to align my answers with their goals.
Used the STAR method to structure my project example.
Outcome: Cleared the round and was scheduled for an on-site interview with a manager.
Round 2 - Technical Interview (On-site):
Questions Asked:
Explain a machine learning model you’ve worked on and its impact.
How would you handle missing data in a dataset?
Write a SQL query to join two tables.
Your Approach:
Focused on explaining the model’s business impact, not just technical details.
Discussed imputation techniques and domain knowledge for missing data.
Practiced SQL queries beforehand to ensure accuracy.
Outcome: Advanced to the next technical round.
Round 3 - Technical Interview (On-site):
Questions Asked:
How do you evaluate the performance of a classification model?
Describe a time you had to explain a technical concept to a non-technical stakeholder.
Solve a case study on optimizing a business process using data science.
Your Approach:
Listed metrics like precision, recall, and ROC curves for model evaluation.
Used a real-world example to demonstrate communication skills.
Structured the case study solution logically, focusing on business outcomes.
Outcome: Received positive feedback and moved to the final HR discussion.
Round 4 - HR Discussion:
Questions Asked:
What are your salary expectations?
How do you handle feedback?
Where do you see yourself in 5 years?
Your Approach:
Researched industry standards for salary and provided a reasonable range.
Emphasized adaptability and growth mindset for feedback and career goals.
Outcome: Received an offer letter from EY.
Preparation Tips:
Brush up on SQL, Python, and machine learning concepts.
Practice explaining technical topics in simple terms.
Review case studies related to business optimization.
Conclusion:
The interview process was thorough but well-structured. Preparing for both technical and behavioral questions was key. I could have practiced more case studies to feel even more confident. For future candidates, focus on clear communication and aligning your answers with EY’s business goals.