Ernst & Young Advisory Services Sdn Bhd Data Scientist Interview Guide 2026

What is a Data Scientist at Ernst & Young Advisory Services Sdn Bhd?

A Data Scientist at Ernst & Young Advisory Services Sdn Bhd plays a pivotal role in transforming data into actionable insights that drive strategic decision-making. Your work will directly influence client outcomes, shaping the way organizations leverage data to mitigate risks, enhance operational efficiency, and innovate solutions. This position is critical not just for the technical expertise it brings, but also for its strategic influence on the firm's advisory capabilities across various sectors.

In this role, you will engage with complex datasets, employing advanced analytics and machine learning techniques to address client-specific challenges. Whether you are developing predictive models, conducting risk assessments, or creating data visualization tools, your contributions will enhance the overall value proposition of Ernst & Young Advisory Services Sdn Bhd. The work environment is dynamic, filled with opportunities to collaborate with multidisciplinary teams, including consultants, engineers, and business analysts, to deliver comprehensive solutions that meet client needs.

Expect to be at the forefront of innovative projects that leverage cutting-edge technologies, such as artificial intelligence and natural language processing, all while working within a culture that emphasizes continuous learning and professional growth.

Common Interview Questions

As you prepare for your interview, it’s important to understand that the questions you will face are reflective of the role and the expectations at Ernst & Young Advisory Services Sdn Bhd. The following are representative questions drawn from various sources, including 1point3acres.com, designed to illustrate common themes rather than serve as a memorization list.

Technical / Domain Questions

These questions assess your foundational knowledge and practical application of data science principles.

Explain the difference between supervised and unsupervised learning.
How do you handle missing data in a dataset?
Describe a project where you applied machine learning techniques. What challenges did you face?
What are some common algorithms used in predictive modeling?
Explain the concept of overfitting and how it can be mitigated.

Behavioral / Leadership

These questions evaluate your interpersonal skills and cultural fit within the organization.

Describe a time when you had to work under pressure. How did you handle it?
How do you prioritize multiple tasks and deadlines?
Can you provide an example of how you resolved a conflict within a team?
What motivates you to perform well in your job?
How do you handle feedback and criticism?

Problem-solving / Case Studies

Expect to engage in scenarios that assess your analytical thinking and problem-solving capabilities.

Given a dataset with sales information, how would you identify trends and anomalies?
How would you approach a situation where a client is skeptical about data-driven decisions?
Describe how you would design an experiment to test the effectiveness of a new marketing strategy.

Coding / Algorithms

Demonstrating your programming skills is crucial, particularly in languages like Python or R.

Write a function to calculate the mean and median of a list of numbers.
How would you optimize a SQL query to retrieve data efficiently?
Explain the steps involved in feature selection for a machine learning model.

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Practice questions from our question bank

Curated questions for Ernst & Young Advisory Services Sdn Bhd from real interviews. Click any question to practice and review the answer.

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Pipelines

Handle Missing Values in ETL

Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.

ETL

Data Wrangling

Quality

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Easy

SQL & Data Manipulation

Handling Missing Values in SQL

Explain how to detect and handle NULL values in SQL using filtering, COALESCE, CASE, and business-aware imputation.

Aggregations

Case When

Data Wrangling

Easy

Model Evaluation

Explain Cross-Validation to Executives

Explain why cross-validation gives a more trustworthy view of model performance than a single strong test split.

Cross-Validation

Accuracy

Calibration

Easy

Model Evaluation

Compare Precision-Recall Tradeoffs

Compare two classifiers with high-precision vs high-recall behavior and recommend the better model under business cost and review-capacity constraints.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate F1 Score Significance in Model Performance

Analyze the significance of the F1 score in a binary classification model for customer churn prediction, and propose improvements.

Accuracy

F1 Score

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Pipelines

Build Data Quality Controls Pipeline

Design a batch ETL pipeline that validates CRM, billing, and product data before loading curated Snowflake tables.

Data Modeling

ETL

Quality

Easy

SQL & Data Manipulation

Classify Orders with CASE WHEN

Explain how CASE WHEN adds conditional logic to SQL queries for labeling, transforming, and aggregating data.

Aggregations

Case When

Data Wrangling

Easy

Pipelines

Ensure Data Quality in ETL

Design a Snowflake ETL pipeline that enforces schema, deduplication, reconciliation, and auditable data quality checks for finance data.

Data Modeling

ETL

Quality

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Model Evaluation

Evaluate Model Metrics for Customer Churn Prediction

Analyze why a customer churn prediction model has low recall despite high precision and propose actionable improvements.

Easy

Metrics

Measure Checkout Funnel Conversion Rate

Define overall and step-level funnel conversion for an e-commerce checkout flow and explain how to diagnose where drop-off occurs.

KPIs

Conversion Rate

Funnel Analysis

Easy

SQL & Data Manipulation

Handling Missing Demographic Data

Explain how to assess, quantify, and handle missing demographic fields in SQL without distorting downstream analysis.

Subqueries

Case When

Data Wrangling

Easy

SQL & Data Manipulation

Detect and Handle Outliers in SQL

Explain common SQL-friendly ways to detect outliers and how to handle them without distorting downstream analysis.

Aggregations

Group By

Data Wrangling

Easy

Machine Learning

Compare Bagging and Boosting for Claims Risk

Explain and compare bagging vs boosting by training tree-based ensembles to predict high-cost insurance claims.

Ensemble Methods

Bias-Variance Tradeoff

Decision Trees

Easy

Statistics & Probability

A/B Test for Ranking Model Value

Use a two-proportion z-test to determine whether a new ranking model significantly improves recommendation CTR in an A/B test.

A/B Testing

Statistical Significance

Experimentation

Easy

Model Evaluation

Choose Metrics for Business Impact

Decide whether precision, recall, F1-score, or RMSE best fits fraud detection and demand forecasting given asymmetric business costs.

Accuracy

Precision

Recall

+2 more

Medium

Product Sense

Investigate Engagement Drop with Stable ARR

Analyze the root cause of a 10% engagement drop while ARR remains flat to inform product strategy.

User Needs

Engagement Metrics

Churn

+2 more

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Getting Ready for Your Interviews

Preparation is key to succeeding in your interviews. Focus on understanding the expectations for the role and how your experiences align with those expectations. Here are the key evaluation criteria to consider:

Role-related knowledge – This criterion gauges your technical expertise in data science. Interviewers will look for your familiarity with statistical methods, machine learning algorithms, and data visualization tools. Strengthen your understanding by reviewing relevant coursework or practical projects you have undertaken.

Problem-solving ability – Your approach to tackling challenges is vital. Demonstrate how you structure problems, analyze data, and develop solutions. Consider using the STAR (Situation, Task, Action, Result) method to articulate your thought process during interviews.

Culture fit / values – Ernst & Young Advisory Services Sdn Bhd values collaboration and integrity. Show how your personal values align with the company’s mission and culture. Be prepared to discuss examples of how you have embodied these values in your professional experiences.

Interview Process Overview

The interview process at Ernst & Young Advisory Services Sdn Bhd is thorough and systematic. Candidates typically start with an initial HR screening, where your qualifications and cultural fit will be assessed. Following this, you can expect a series of technical interviews focusing on your data science expertise and problem-solving skills. These may include coding assessments, case studies, and discussions about your previous projects.

Interviews are structured to evaluate not only your technical abilities but also your soft skills and how well you can collaborate with others. The pace is rigorous, and thorough preparation is essential to showcase your capabilities effectively. Ernst & Young emphasizes a collaborative approach, so expect to engage in discussions that highlight your ability to work within teams.

The visual timeline illustrates the various stages of the interview process, from initial screenings to technical evaluations and final discussions. Use this timeline to manage your preparation and allocate sufficient time to each stage.

Deep Dive into Evaluation Areas

To excel in your interviews, understanding how you will be evaluated is crucial. Here are several key evaluation areas:

Technical Skills

Technical skills are foundational for a Data Scientist. Interviewers will assess your proficiency in programming languages (such as Python and R), as well as your understanding of machine learning frameworks.

Data Manipulation – Expect questions on how to clean, transform, and analyze data using tools like Pandas or NumPy.
Statistical Analysis – Be prepared to explain concepts such as hypothesis testing, regression analysis, and A/B testing.
Machine Learning – Familiarity with algorithms such as decision trees, neural networks, and clustering methods is critical.

Problem-solving Ability

Your problem-solving skills will be evaluated through case studies and hypothetical scenarios.

Analytical Thinking – Demonstrate how you approach complex data challenges and derive insights.
Creativity – Be ready to discuss innovative solutions you've implemented in past projects.
Quantitative Reasoning – Expect to solve numerical problems on the spot, showcasing your mathematical skills.

Communication Skills

Effective communication is essential for a Data Scientist, as you will need to convey complex ideas to non-technical stakeholders.

Presentation Skills – You might be asked to present a data-driven insight or project summary.
Collaboration – Discuss how you've worked with cross-functional teams to achieve project goals.
Clarity – Ensure you can articulate technical concepts in a way that is accessible to diverse audiences.

Key Responsibilities

As a Data Scientist at Ernst & Young Advisory Services Sdn Bhd, you will have a variety of responsibilities that center around leveraging data to drive business outcomes. Your primary tasks will include:

Developing predictive models and algorithms that provide actionable insights for clients.
Collaborating with cross-functional teams to identify data-driven solutions that address client needs.
Conducting data analysis to identify trends, patterns, and opportunities for improvement.
Presenting findings and recommendations to stakeholders, ensuring clarity and actionable direction.
Continuously updating your skills and knowledge to keep pace with the rapidly evolving field of data science.

This position requires a mix of independent work and active collaboration with others, making it essential to build strong relationships across different teams.

Role Requirements & Qualifications

A strong candidate for the Data Scientist role at Ernst & Young Advisory Services Sdn Bhd should possess a mix of technical expertise and interpersonal skills. Here’s what you should aim for:

Must-have skills:
- Proficiency in programming languages such as Python and R.
- Experience with machine learning frameworks (e.g., TensorFlow, scikit-learn).
- Strong understanding of SQL for data retrieval and manipulation.
- Knowledge of data visualization tools (e.g., Tableau, Power BI).
Nice-to-have skills:
- Familiarity with big data technologies (e.g., Hadoop, Spark).
- Experience in cloud services (e.g., AWS, Azure).
- Knowledge of natural language processing techniques.

Candidates should ideally have 2-5 years of experience in a data science or analytics role, along with strong analytical and communication skills to effectively convey insights.

Frequently Asked Questions

Q: How difficult is the interview process?
The interview process is considered rigorous, with a blend of technical and behavioral assessments. Candidates typically prepare for several weeks to familiarize themselves with relevant concepts and practices.

Q: What distinguishes successful candidates?
Successful candidates demonstrate a solid foundation in data science, along with effective communication skills and the ability to work collaboratively. They also showcase a genuine passion for data and a proactive approach to problem-solving.

Q: What is the company culture like?
Ernst & Young Advisory Services Sdn Bhd fosters a culture of collaboration, integrity, and continuous learning. Employees are encouraged to share knowledge and learn from one another in a supportive environment.

Q: What is the typical timeline from initial screen to offer?
The interview process can take several weeks, typically ranging from one to three months, depending on the number of candidates and scheduling logistics.

Q: Are there opportunities for remote work?
While many roles may allow for remote work, the exact arrangements can vary by team and project requirements. Be sure to clarify expectations during your interview.

Other General Tips

Understand the business: Familiarize yourself with Ernst & Young Advisory Services Sdn Bhd’s offerings and recent projects to relate your answers to the company’s strategic goals.
Practice coding: Be prepared for live coding interviews or take-home assignments. Practicing on platforms like LeetCode or HackerRank can help sharpen your skills.
Showcase your projects: Bring examples of your previous work or projects to discuss during the interview. Highlight your contributions and the impact of your work.
Ask insightful questions: Prepare thoughtful questions for your interviewers to show your interest in the role and the company's direction.

Interview Guides

Ernst & Young Advisory Services Sdn Bhd

What is a Data Scientist at Ernst & Young Advisory Services Sdn Bhd?

Common Interview Questions

Technical / Domain Questions

Behavioral / Leadership

Problem-solving / Case Studies

Coding / Algorithms

See every interview question for this role

Practice questions from our question bank

Sign up to see all questions

Getting Ready for Your Interviews

Interview Process Overview

Deep Dive into Evaluation Areas

Technical Skills

Problem-solving Ability

Communication Skills

Key Responsibilities

Role Requirements & Qualifications

Frequently Asked Questions

Other General Tips

Note

Summary & Next Steps

See every interview question for this role