Senior ML Engineer

Job not on LinkedIn

🔥 14 hours ago

🇪🇬 Egypt – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 11%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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SSC HR Solutions

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2006

💼 Consulting

📦 Logistics

🏥 Healthcare

Consulting • Logistics • Healthcare

SSC HR Solutions is a full-service HR consultancy that provides comprehensive human resource solutions tailored to meet the specific talent growth needs of businesses. With operating locations across Egypt, United Arab Emirates, Saudi Arabia, and Bahrain, SSC HR Solutions specializes in employee management, from hiring to termination, ensuring that businesses thrive in a competitive landscape. The company prides itself on understanding client needs and delivering high-quality services that foster growth and partnership.

📋 Description

• Own predictive customer scores shipped with the product, including churn, propensity, lifetime value, spend intent, and response scoring. • Apply machine learning and tabular predictive modelling to customer data. • Build and manage production models supporting customer prediction and scoring. • Manage the full model lifecycle, including training, deployment, retraining, monitoring, evaluation, and calibration. • Operate models within a self-managed data platform environment. • Support predictive use cases focused on production models.

🎯 Requirements

• Applied machine learning with models running in production, not research or proof of concept. • Deep hands on with tabular predictive modelling on customer data. • Has built churn or propensity models in telco, banking, or retail. • Training, deployment, and retraining pipelines in a self managed environment. • MLOps practice: model registry, versioning, retraining, monitoring, and drift detection. • Comfortable working inside a data platform rather than a notebook. • Uplift or causal modelling for incremental targeting. • Feature store design. • Working with commercial stakeholders on what a prediction is used for.