Job Description
We are looking for a Senior Machine Learning Engineer with strong experience in the Telecommunications (Telco) domain to design, develop, deploy, and maintain production-ready machine learning solutions.
The ideal candidate will have hands-on experience in ML model development, feature engineering, deployment, monitoring, and retraining, with a strong understanding of MLOps practices and the end-to-end machine learning lifecycle.
Key Responsibilities
- Develop and deploy production-grade machine learning models for Telco use cases.
- Build ML solutions for use cases such as 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.
Requirements
Requirements
- 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, such as:
- Churn prediction
- Customer clustering/segmentation
- Demand forecasting
- 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
- 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.