Societe Generale Global Solution Centre Data Scientist Interview Questions and Answers

The choice of ML model depends on the problem, data, and desired outcome.

• Consider the problem type: classification, regression, clustering, etc.

• Analyze the data: size, quality, features, and target variable.

• Evaluate model performance: accuracy, precision, recall, F1-score.

• Consider interpretability, scalability, and computational requirements.

• Experiment with multiple models: decision trees, SVM, neural networks, etc.

• Use...read more

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...read more

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.

I am a data scientist with expertise in statistical analysis, machine learning, and data visualization, passionate about solving complex problems.

• Projects include predictive modeling for sales forecasting using regression analysis.

• Developed a classification model for customer segmentation using k-means clustering.

• Conducted A/B testing to evaluate the effectiveness of marketing strategies.

• Utilized Python and R for data ...read more