Senior ML Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 13d ago
The date the source published, not the day we noticed it (2026-09-19). Last seen at its source just now.
We have tracked this listing since 19 Sep 2026 (12 days). The employer's own board has carried it every time we have read it, most recently just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Egypt
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay €68.8k–104k/yr
Middle 50% of 43 listings that do state pay — Engineering · Senior · EMEA · EUR/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
workable employer's own board first seen 12d ago · last seen just now
The listing
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.