- 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
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