Senior ML Engineer

This is a fully remote position, open to applicants in Egypt.

📋 Description

• Design and implement production-level machine learning models for telecommunications applications.

• Create ML solutions aimed at predicting customer churn, segmenting customers, and forecasting demand.

• Prepare, clean, and transform extensive customer datasets for analysis.

• Conduct feature engineering to develop essential features for machine learning models.

• Train, validate, and assess both supervised and unsupervised machine learning models.

• Utilize Python for data preparation, model development, validation, and automation tasks.

• Employ SQL to access, extract, transform, and process data from diverse sources.

• Establish and maintain MLOps pipelines throughout the machine learning lifecycle.

• Oversee model versioning, deployment, production monitoring, and retraining efforts.

• Track model performance and address data/model drift in production, implementing necessary corrective measures.

• Work collaboratively with data engineers, data scientists, and business stakeholders to provide scalable ML solutions.

• Ensure that ML models are dependable, maintainable, and appropriate for production settings.

• Continuously enhance existing models, features, and ML workflows based on outcomes from production.

⛳️ Requirements

• A minimum of 4 years' experience in Machine Learning, Data Science, or a related domain.

• Experience in the Telecommunications sector is essential.

• Demonstrated ability to build and deploy production ML models for telecommunications applications, including churn prediction, customer segmentation/clustering, demand forecasting, and customer behavior analysis.

• Solid understanding of both supervised and unsupervised machine learning methodologies.

• Extensive hands-on experience using Python for data preparation, feature engineering, and ML model development.

• Strong SQL capabilities for accessing, processing, and analyzing customer data.

• Practical experience with feature engineering and preparing customer data.

• Relevant MLOps experience, covering model versioning, deployment, production monitoring, and retraining.

• Familiarity with MLflow or a comparable MLOps/model lifecycle management platform.

• Proven track record of transitioning ML models from development to production deployment and subsequent monitoring.

• Ability to assess model performance and pinpoint areas for enhancement.

• Comprehensive understanding of the complete machine learning lifecycle.

• Experience with Dataiku or similar enterprise ML platforms is advantageous.

• Knowledge of Spark, Feature Stores, or uplift modeling is a plus.

🏝️ Benefits

• Competitive salary and performance-based incentives.

• Opportunities for professional development and skill enhancement.

• Flexible working hours and remote work options.

• Comprehensive health and wellness benefits.

• Collaborative and inclusive work environment.