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Ernst & Young Senior Data Scientist Interview Questions, Process, and Tips
Top Ernst & Young Senior Data Scientist Interview Questions and Answers
- Q1. How do you handle large amount of data in financial domain?
- Q2. Tell me about anomaly detection problem? What is LSTM? Why do you need BERT in the chatbot?
- Q3. Tell me about preprocessing techniques? How can you resolve over fitting problem?
Ernst & Young Senior Data Scientist Interview Experiences
3 interviews found
Anonymous
I applied via Recruitment Consulltant and was interviewed in Apr 2024. There were 3 interview rounds.
(2 Questions)
- Q1. Ml based questions, linear regression, classifications, regularizations, clustering based questions
- Q2. Coding test based on a data frame
(2 Questions)
- Q1. Techno managerial round
- Q2. Lots of ML use case based questions
(2 Questions)
- Q1. Interview with Director
- Q2. Questions based on projects done on cv
I applied via Referral and was interviewed in Mar 2024. There was 1 interview round.
(1 Question)
- Q1. Coding Basic ML and MLOps
I applied via Approached by Company and was interviewed before Feb 2021. There were 2 interview rounds.
(2 Questions)
- Q1. Tell me about anomaly detection problem? What is LSTM? Why do you need BERT in the chatbot?
- Ans.
Anomaly detection is identifying unusual patterns in data. LSTM is a type of neural network used for sequence prediction. BERT is used in chatbots for natural language processing.
Anomaly detection involves identifying patterns in data that deviate from the norm
LSTM is a type of neural network that is used for sequence prediction and can handle long-term dependencies
BERT is a pre-trained language model used for natural ...
- Q2. Tell me about preprocessing techniques? How can you resolve over fitting problem?
- Ans.
Preprocessing techniques include data cleaning, normalization, encoding, and feature scaling. Overfitting can be resolved by using techniques like cross-validation, regularization, and early stopping.
Data cleaning involves removing missing values, outliers, and duplicates
Normalization scales the data to a range of 0 to 1
Encoding converts categorical variables into numerical values
Feature scaling standardizes the range ...
(2 Questions)
- Q1. How do you handle large amount of data in financial domain?
- Ans.
I handle large amount of financial data by using distributed computing and parallel processing.
Use distributed computing frameworks like Hadoop or Spark to handle large datasets
Implement parallel processing to speed up data processing
Use cloud-based solutions like AWS or Azure for scalability
Optimize data storage and retrieval using compression and indexing techniques
Ensure data security and compliance with regulations
- Q2. How would you use handle outlier and unbalanced dataset?
- Ans.
Outliers can be handled by removing or transforming them. Unbalanced datasets can be handled by resampling techniques.
For outliers, use statistical methods like z-score or IQR to identify and remove them.
For unbalanced datasets, use techniques like oversampling, undersampling, or SMOTE to balance the classes.
For regression problems, use robust regression techniques like Ridge or Lasso to handle outliers.
For classificat...
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Skills evaluated in this interview
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Bhindia Binoy
I applied via Approached by Company and was interviewed in Mar 2024. There were 5 interview rounds.
(1 Question)
- Q1. MLOps vs DevOps
- Ans.
MLOps focuses on machine learning model deployment and management, while DevOps focuses on software development and IT operations.
MLOps is specifically tailored for machine learning models, ensuring they are deployed, monitored, and managed effectively.
DevOps is a broader practice that focuses on collaboration between development and operations teams to automate and streamline the software development process.
MLOps inc...
(1 Question)
- Q1. Docker Containers
(1 Question)
- Q1. ML Deployment, Docker Containers
(1 Question)
- Q1. Compensation, Fixed Salary
(1 Question)
- Q1. Coding in Python using explode
- Ans.
Explode function in Python is used to split a string into a list of strings based on a specified delimiter.
Use the explode function from the pandas library to split a string column into multiple rows in a DataFrame.
Specify the delimiter parameter to define how the string should be split.
For example, df['column'].str.split('delimiter').explode() will split the strings in 'column' based on 'delimiter' and create a new ro
Interview Preparation Tips
The candidate gave a counteroffer letter to HR and HR was fully aware of the joining date in that company. But still, HR made a promise on the phone she would give the offer letter a day or two days after the final round ie, HR. The next day HR called me to go through another round with the director and later said you got rejected. The candidate rejected the counteroffer letter and listened to the words of Deloitte HR's promise. So never believe these HR representatives of Deloitte, they will make you go jobless. Very PATHETIC experience. BEWARE OF SUCH FRAUDSTERS.
Skills evaluated in this interview
Selected
I applied via Approached by Company and was interviewed before May 2023. There were 4 interview rounds.
(4 Questions)
- Q1. Why you want to join this company?
- Q2. What is your current CTC and expectations
- Q3. What is your notice period
- Q4. Do you have any offers in hand
(2 Questions)
- Q1. Why you chose this ML model over others
- Ans.
I chose this ML model because of its high accuracy and interpretability.
The chosen model has shown superior performance in cross-validation compared to other models.
The model's interpretability allows for easier understanding of feature importance and decision-making processes.
The chosen model is well-suited for the specific problem domain and dataset characteristics.
For example, I chose a Random Forest model over a Ne...
- Q2. Accuracy and how you know your model does well
(2 Questions)
- Q1. What is Shap value and plot
- Ans.
Shap values explain individual predictions in machine learning models.
Shap values quantify the impact of each feature on a model's predictions.
They help in understanding the importance of different features in the model.
Shap plots visually represent the impact of features on predictions.
They can be used to explain black-box models like XGBoost or neural networks.
- Q2. Explain tree based model and hyperparameters
- Ans.
Tree based models use decision trees to make predictions, with hyperparameters controlling the model's behavior.
Tree based models are a type of machine learning model that uses decision trees to make predictions.
Hyperparameters are settings that control the behavior of the model, such as the maximum depth of the tree or the minimum number of samples required to split a node.
Examples of tree based models include Random
Pharma case study questions
Skills evaluated in this interview
Hemant Yadav
Selected
I applied via Approached by Company and was interviewed in Oct 2024. There were 2 interview rounds.
Combination logic on python
(1 Question)
- Q1. Explain classification
- Ans.
Classification is a machine learning technique used to categorize data into different classes or categories based on past observations.
Classification involves training a model on labeled data to predict the class of new, unseen data points.
Common algorithms for classification include logistic regression, decision trees, support vector machines, and k-nearest neighbors.
Examples of classification tasks include spam email...
(1 Question)
- Q1. ML, DL, Data Science, Python
(1 Question)
- Q1. AI,ML, Data Science, Use cases
Machine Learning, Metrics
Selected
I applied via Approached by Company and was interviewed before May 2023. There were 2 interview rounds.
(1 Question)
- Q1. 1. What is the role of beta value in Logistic regression? 2. What is bias variance trade off? 3. How did you decide on the list of variables that would be used in a model?
- Ans.
Beta value in logistic regression measures the impact of independent variables on the log odds of the dependent variable.
Beta value indicates the strength and direction of the relationship between the independent variables and the log odds of the dependent variable.
A positive beta value suggests that as the independent variable increases, the log odds of the dependent variable also increase.
A negative beta value sugges...
1. 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?
Interview Preparation Tips
Skills evaluated in this interview
Selected
I applied via Approached by Company and was interviewed before Sep 2023. There were 3 interview rounds.
Simple Data Science Case Study
(1 Question)
- Q1. Python knowledge related questions
Data Science Case Study
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