- Develop and deploy production-grade machine learning models for telecommunications use cases.
- Build solutions for customer churn prediction, customer segmentation or clustering, and demand forecasting.
- Prepare, clean, transform, and analyze large customer datasets.
- Perform feature engineering and develop relevant model features.
- Train, validate, and evaluate supervised and unsupervised machine learning models.
- Use Python for data preparation, model development, validation, and automation.
- Use SQL to access, extract, transform, and process data from various sources.
- Implement and maintain MLOps pipelines across the machine learning lifecycle.
- Manage model versioning, deployment, production monitoring, and retraining.
- Monitor production model performance and data or model drift, and take corrective actions.
- Collaborate with data engineers, data scientists, and business stakeholders to deliver scalable ML solutions.
- Improve models, features, and ML workflows based on production results.
PythonSQLMachine Learning+2 more