Senior ML Engineer
SSC HR Solutions Open · verified Oct 22, 2026 At a glance
Senior Machine Learning Engineering role at SSC HR Solutions. Remote, Egypt.
Pay not stated ML Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build customer scoring models for churn, propensity, lifetime value, spend intent and response, then keep them monitored in production
- Run models through training, deployment, retraining, evaluation and calibration in a self-managed data platform
- Maintain model registries, versioning, monitoring and drift detection as part of the production MLOps workflow
- Work with commercial stakeholders to connect predictions to their intended use
What you bring
- Hands-on experience applying machine learning to tabular customer data, with models operating in production
- Experience building churn or propensity models in telco, banking or retail
- Practical experience with training, deployment and retraining pipelines in a self-managed environment
- Familiarity with model registries, version control, monitoring and drift detection
- Comfort working within a data platform rather than relying on notebooks
Who this fits
This role suits a senior ML engineer focused on production customer prediction and scoring rather than research or proof-of-concept work. The position is remote and based in Cairo, Egypt.
From the employer
Description
Owns the predictive customer scores that ship with the product: churn, propensity, lifetime value, spend intent, and response scoring, from training through to monitoring. Applies machine learning and tabular predictive modelling to customer data, building and managing production models that support customer prediction and scoring. Works across the full model lifecycle, including model training, deployment, retraining, monitoring, evaluation, and calibration within a self-managed data platform environment. Supports predictive use cases such as churn, propensity, lifetime value, spend intent, and response scoring, with a focus on models running in production.
Requirements
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.