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Barclays Shared Services Data Scientist Interview Questions and Answers
Barclays Shared Services Data Scientist Interview Experiences
1 interview found
Anonymous
Selected
I applied via Referral and was interviewed before Aug 2023. There were 2 interview rounds.
(2 Questions)
- Q1. What is regression , classification
- Ans.
Regression is a statistical method to predict continuous outcomes, while classification is used to predict categorical outcomes.
Regression is used when the target variable is continuous, such as predicting house prices based on features like size and location.
Classification is used when the target variable is categorical, like predicting whether an email is spam or not based on its content.
Regression models include lin...
- Q2. NLP techniques and text classification algorithms and techniques like word embeddings
(2 Questions)
- Q1. Hyper parameters for classification algorithms
- Ans.
Hyper parameters are settings that are set before the learning process begins and affect the learning process itself.
Hyper parameters are not learned during the training process, but are set before training begins.
They control the learning process and impact the performance of the model.
Examples include learning rate, number of hidden layers, and batch size in neural networks.
- Q2. How to improve efficiency of models
- Ans.
Improving model efficiency involves feature selection, hyperparameter tuning, and ensemble methods.
Perform feature selection to reduce dimensionality and focus on relevant features
Optimize hyperparameters using techniques like grid search or random search
Utilize ensemble methods like bagging or boosting to improve model performance
Consider using more advanced algorithms like deep learning for complex data patterns
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I appeared for an interview before Jul 2021.
(2 Questions)
- Q1. Model evaluation and performance metrices
- Q2. Explaination of bagging and boosting techniques
- Ans.
Bagging and boosting are ensemble techniques used to improve the accuracy of machine learning models.
Bagging involves training multiple models on different subsets of the training data and then combining their predictions through voting or averaging.
Boosting involves iteratively training models on the same data, with each subsequent model focusing on the samples that the previous models misclassified.
Bagging reduces va...
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Skills evaluated in this interview
I applied via Recruitment Consultant and was interviewed in Mar 2021. There were 3 interview rounds.
Interview Questionnaire
2 Questions
- Q1. Super Quick Case Study on Sales
- Q2. Questions on Machine learning Projects in your Resume
Interview Preparation Tips
Other topics: SQL, Excel, Pivot tables
I applied via Naukri.com and was interviewed before Jul 2021. There were 3 interview rounds.
(1 Question)
- Q1. NLP and Computer Vision based Question were asked.
(1 Question)
- Q1. General Questions on Machine Learning and deployments technique
(1 Question)
- Q1. Salary Negotiations and joining discussion
Interview Preparation Tips
I applied via Approached by Company and was interviewed in Apr 2024. There was 1 interview round.
(1 Question)
- Q1. Conceptual Questions on SQL and Python
Interview Preparation Tips
I applied via Job Portal and was interviewed before Jan 2021. There was 1 interview round.
Interview Questionnaire
3 Questions
- Q1. Dataset were give and asked to get an overview of data on IDE of your choice and then was asked to explain different steps required for solving classification problem w.r.t. shared data.
- Ans.
Overview of data and steps for solving a classification problem using an IDE.
1. Data Exploration: Load the dataset and check for missing values, data types, and basic statistics.
2. Data Preprocessing: Handle missing values (e.g., imputation), encode categorical variables (e.g., one-hot encoding), and normalize/scale features.
3. Train-Test Split: Divide the dataset into training and testing sets (e.g., 80% train, 20% te...
- Q2. Was asked to explain my project and asked different data science techniques like dimensionality reduction, data cleaning approaches etc.. Was also asked to explain internals of algorithm used.
- Q3. Knowledge of any cloud platform
- Ans.
I have experience with cloud platforms like AWS, Azure, and Google Cloud for data storage, processing, and machine learning tasks.
Familiar with AWS services like S3 for storage and EC2 for computing.
Experience using Azure Machine Learning for building predictive models.
Utilized Google Cloud BigQuery for large-scale data analysis.
Implemented data pipelines using AWS Lambda and Glue for ETL processes.
Interview Preparation Tips
Selected
I applied via Campus Placement and was interviewed before Feb 2023. There were 2 interview rounds.
Leet code practice solve medium difficulty questions
(1 Question)
- Q1. Linear regression logistic regression deep learning nlp
Selected
I applied via Approached by Company and was interviewed before Jun 2022. There were 4 interview rounds.
Quant, Reasoning and python based MCQs
(3 Questions)
- Q1. Question on your past work experience. Can go deep down with respect to your prior work experience
- Q2. In what project have you been involved and what were your roles and responsibility
- Q3. Data Science project pipeline ,what components are involved , step by step process
- Ans.
Data science project pipeline involves multiple components and follows a step-by-step process.
1. Define the problem statement and objectives of the project.
2. Collect and preprocess the data needed for analysis.
3. Explore and visualize the data to gain insights.
4. Build and train machine learning models to solve the problem.
5. Evaluate the models using appropriate metrics.
6. Deploy the model into production and monitor...
(1 Question)
- Q1. Your prior experience and salary expectations
Interview Preparation Tips
I applied via Naukri.com and was interviewed in Sep 2024. There was 1 interview round.
(2 Questions)
- Q1. About the project
- Q2. Evaluation metrics in linear regression, Assumption of linear regression,
- Ans.
Evaluation metrics and assumptions in linear regression
Evaluation metrics in linear regression include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared, and Adjusted R-squared.
Assumptions of linear regression include linearity, independence, homoscedasticity, and normality of residuals.
Example: MSE = sum((actual - predicted)^2) / n
Skills evaluated in this interview
Interview Questionnaire
1 Question
- Q1. Diffe heilman algorithm
- Ans.
Diffie-Hellman algorithm is a key exchange protocol used to securely exchange cryptographic keys over a public channel.
It is based on the concept of discrete logarithm problem.
It involves two parties, Alice and Bob, who generate their own private and public keys.
The public keys are exchanged and used to generate a shared secret key.
The shared secret key is used for encryption and decryption of messages.
It is widely use...
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