Barclays Shared Services Data Scientist Interview Questions and Answers

Q1. Fundamentals of ML, classical ML
Ans. 
Fundamentals of classical machine learning
• Classical machine learning involves algorithms that learn from data and make predictions or decisions.

• Common algorithms include linear regression, decision trees, support vector machines, and k-nearest neighbors.

• Key concepts include training data, testing data, model evaluation, and hyperparameter tuning.

• Classical ML is often used for tasks like classification, regression, clus
Answered by AI
Add your answer

Q1. Research paper discussion
Add your answer

Q1. Question related to CNN in details, Different ML algorithms, Questions related to pandas
Add your answer

Q1. Question related to MLOps, GitHub
Add your answer

Q1. Salary expectations
Add your answer

Coding test with mcq - aptitude , coding , stats , ml

Q1. Project related like deployement
Add your answer

Quant, Reasoning and python based MCQs

Q1. Question on your past work experience. Can go deep down with respect to your prior work experience
Add your answer
Q2. In what project have you been involved and what were your roles and responsibility
Add your answer
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...read more
Answered by AI
Add your answer

Q1. Your prior experience and salary expectations
Add your answer

They gave a span of 3 days to build an AI-powered webapp

Q1. How would you go about learning a new skill
Add your answer
Q2. Experience in cloud technologies
Ans. 
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
• Experience in setting up and managing virtual machines, storage, and networking in cloud environments

• Knowledge of cloud services like EC2, S3, RDS, and Lambda

• Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
Answered by AI
Add your answer

Q1. Tell me about yourself
Add your answer
Q2. Project details and challenges faced in the project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify key factors influencing churn

• Built and fine-tuned machine learning models to predict customer churn

• Challenges included imbalanced data, feature engineering, and model interpretability
Answered by AI
Add your answer

Q1. Model evaluation and performance metrices
Add your answer
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...read more
Answered by AI
Add your answer

Leet code practice solve medium difficulty questions

Q1. Linear regression logistic regression deep learning nlp
Add your answer