• Develop, implement, and optimize time series forecasting models.
  
• Build and maintain end-to-end ML pipelines for data ingestion, preprocessing, model training, and deployment.
  
• Apply machine learning algorithms (regression, classification, ensemble methods, boosting, etc.) to business use cases.
  
• Design and train deep learning architectures (LSTM, GRU, CNNs, Transformers) for sequential and structured data.
  
• Perform feature engineering, hyperparameter tuning, and model evaluation.
  
• Collaborate with data engineers to ensure data quality, scalability, and efficient model deployment.
  
• Deploy models to production environments.
  
• Familiarity with MLOps best practices (versioning, monitoring, CI/CD for ML).
  
• Monitor, retrain, and maintain models for continuous improvement and drift detection.
  
• Document processes, models, and findings to ensure knowledge sharing and reproducibility