Senior ML Engineer

• Develop and deploy production-grade machine learning models for Telco use cases
• Build ML solutions for customer churn prediction, customer segmentation/clustering, and demand forecasting
• Prepare, clean, transform, and analyze large customer datasets
• Perform feature engineering and develop relevant features for machine learning models
• 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 ML lifecycle
• Manage model versioning, deployment, production monitoring, and retraining
• Monitor model performance and data/model drift in production and take appropriate corrective actions
• Collaborate with data engineers, data scientists, and business stakeholders to deliver scalable ML solutions
• Ensure ML models are reliable, maintainable, and suitable for production environments
• Continuously improve existing models, features, and ML workflows based on production results

• 4+ years of experience in Machine Learning, Data Science, or a related field
• Telco/Telecommunications domain experience is a MUST
• Proven experience building and deploying production ML models for Telco use cases, including churn prediction, customer clustering/segmentation, demand forecasting, and customer behavior prediction
• Strong knowledge of supervised and unsupervised machine learning techniques
• Strong hands-on experience with Python for data preparation, feature engineering, and ML model development
• Strong SQL skills for accessing, processing, and analyzing customer data
• Hands-on experience with feature engineering and customer data preparation
• Practical MLOps experience, including model versioning, model deployment, production monitoring, and model retraining
• Experience with MLflow or an equivalent MLOps/model lifecycle management platform
• Experience taking ML models from development through production deployment and ongoing monitoring
• Ability to evaluate model performance and identify opportunities for model improvement
• Strong understanding of the end-to-end machine learning lifecycle
• Experience with Dataiku or equivalent enterprise ML platforms is a plus
• Experience with Spark, Feature Stores, or uplift modelling is a plus