Senior ML Engineer

SSC HR SolutionsTelecommunications

Workable workplace: remote; Workable locations: Cairo, Cairo Governorate, EgyptFull-TimeSenior

Salary not disclosed

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Job Details

Experience: 4+ years of experience in Machine Learning, Data Science, or a related field.
Required Skills: PythonSQLMachine LearningMLFlowMLOps

Bring 4+ years of experience in Machine Learning, Data Science, or a related field.
Have telecommunications domain experience; this is a must.
Have proven experience building and deploying production ML models for telecommunications use cases.
Have strong knowledge of supervised and unsupervised machine learning techniques.
Have strong hands-on Python experience for data preparation, feature engineering, and ML model development.
Have strong SQL skills for accessing, processing, and analyzing customer data.
Have hands-on experience with feature engineering and customer data preparation.
Have practical MLOps experience, including model versioning, deployment, production monitoring, and retraining.
Have experience with MLflow or an equivalent MLOps or model lifecycle management platform.
Have experience taking ML models from development through production deployment and ongoing monitoring.
Be able to evaluate model performance and identify opportunities for improvement.
Have a 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.

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.

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