Data Engineers ( Data Scientists)- multiple positions

Data Collection, Cleaning & Preprocessing Collect and extract structured and unstructured data from various sources including Azure SQL, Excel, and APIs. Clean and preprocess raw data using Python (Pandas, NumPy) to prepare for analysis and modeling. Perform exploratory data analysis (EDA) to identify trends and inconsistencies. Data Analysis & Feature Engineering Apply statistical techniques and feature selection to prepare data for model training. Use correlation analysis, data normalization, and variable encoding to optimize datasets. Support development of predictive models in collaboration with senior data scientists. Model Training & Validation Train machine learning models using Scikit-learn and Azure Machine Learning Studio. Evaluate models using metrics such as accuracy, precision, recall, F1-score. Perform cross-validation and hyperparameter tuning using grid search techniques. Visualization & Reporting Create data visualizations and dashboards using Power BI and Matplotlib. Translate technical results into business insights for stakeholders. Develop automated reports using Azure Logic Apps and Python scripts. Model Deployment & Monitoring Assist in deploying models to Azure ML endpoints for production use. Monitor model performance and data drift using Azure Application Insights. Schedule retraining and scoring pipelines with Azure Data Factory. Documentation, QA & Collaboration Document data science workflows, assumptions, and model version history. Write and review code in Git repositories; follow best practices for version control. Participate in Agile ceremonies (standups, sprint reviews) with data and engineering teams. Business Support & Communication Work with business users to understand their data needs and translate into analytical solutions. Present results in meetings and contribute to decision-making processes.