Machine Learning Engineer

You will take machine-learning use cases from idea to production and build the dashboards and reports that decision-makers use every day. Expect around twenty use cases and twenty dashboards over the year, on Python and Dataiku for the models and Qlik, Power BI and Python for the visuals. This role covers the data-science and analysis side of the project as well as the BI. What you will do - Qualify and prioritise use cases with the client, then build, deploy and monitor the models. - Do the statistical analysis behind each use case and explain the results to people who are not technical. - Design and build dashboards and automated reports in Arabic and English. - Train only on masked or de-identified data, and document the features and data you used. - Keep models and dashboards healthy: retraining, drift monitoring, scheduled refreshes, performance. - Help write the monthly progress report for the AI and dashboard work. What you need - The Python ML stack (pandas, scikit-learn, and one of PyTorch, TensorFlow or XGBoost), with models you have actually run in production - Forecasting, segmentation, recommendation and NLP use cases - MLOps: reproducible training, deployment, monitoring, retraining - Statistical analysis and experiment design - Qlik Sense: load scripts, associative model, set analysis, section access, report automation - Power BI: data model, DAX, Power Query, row-level security, scheduled refresh - Python reporting and visualisation (Plotly or Dash, Streamlit or similar) - Right-to-left, bilingual dashboard design - Privacy-aware practice: masking and de-identification Good to have - Arabic NLP; Vertex AI or BigQuery ML - Marketing-mix, attribution or uplift modelling - Qlik or Power BI certification; Google Professional Machine Learning Engineer

Qualify and prioritise use cases with the client, then build, deploy and monitor the models. Do the statistical analysis behind each use case and explain the results to people who are not technical. Design and build dashboards and automated reports in Arabic and English. Train only on masked or de-identified data, and document the features and data you used. Keep models and dashboards healthy: retraining, drift monitoring, scheduled refreshes, performance. Help write the monthly progress report for the AI and dashboard work.