Description: Required Education: Bachelor's degree in Computer Science, Data Engineering, Software Engineering, or related fields. Required Knowledge: * Python programming (library installation, logic, and best practices). * Data processing and analysis (table relationships, data quality, and optimization). * Python data science libraries (version and dependency management). * Relational databases and SQL. * Data orchestration and transformation (e.g., Apache Airflow). * Management and maintenance of production models. * Logical and analytical reasoning. * Experience with cloud platforms (Azure, AWS, GCP) for ML/Analytics. * Machine learning frameworks (Scikit-learn, TensorFlow, PyTorch). * Databricks integration with Azure DevOps. * Pre-trained models and AutoML. * Techniques for outlier detection and handling. * BI tools and data visualization. Key Responsibilities: * Lead end-to-end data analysis and forecasting projects across the development lifecycle. * Collaborate with business units to understand requirements and propose innovative, data-driven solutions. * Apply advanced analytical and forecasting techniques, including ML, neural networks for time series and classification. * Manage the model lifecycle, clearly communicating results and performance to stakeholders. * Deploy ML models and maintain robust Machine Learning pipelines (MLOps). * Capture, structure, and integrate new data into Data Warehouses and Data Lakes, ensuring governance and security. * Ensure data quality, integrity, and availability for Machine Learning and Artificial Intelligence models. * Optimize Data Lake infrastructure performance and resource utilization, promoting scalability and efficiency. * Manage and monitor production models, ensuring high availability and performance (uptime). * Develop and automate ML workflows and infrastructure, accelerating project delivery from development to production. * Detect, analyze, and address outliers or data quality variations, enhancing result reliability. 2512060202191784572