Machine Learning Engineer
Role Overview
Take machine-learning use cases from idea to production and build dashboards and reports for decision-makers.
The work spans data science, analysis, and business intelligence, with an anticipated 20 use cases and 20 dashboards over the year.
Key Skills for This Role
Full Job Posting
Role overview
Take machine-learning use cases from idea to production and build dashboards and reports for decision-makers.
The work spans data science, analysis, and business intelligence, with an anticipated 20 use cases and 20 dashboards over the year.
Responsibilities
- Qualify and prioritize client use cases; build, deploy, and monitor models.
- Perform statistical analysis and explain results to nontechnical audiences.
- Build Arabic- and English-language dashboards and automated reports.
- Use masked or de-identified training data and document features and data used.
- Maintain models and dashboards through retraining, drift monitoring, scheduled refreshes, and performance checks.
- Help prepare monthly progress reports for AI and dashboard work.
Required qualifications
- Production ML experience with Python, pandas, scikit-learn, and one of PyTorch, TensorFlow, or XGBoost.
- Experience with forecasting, segmentation, recommendation, and NLP use cases; MLOps; statistical analysis; and experiment design.
- Qlik Sense expertise: load scripts, associative model, set analysis, section access, and report automation.
- Power BI expertise: data model, DAX, Power Query, row-level security, and scheduled refresh.
- Python reporting and visualization with Plotly or Dash, Streamlit, or similar.
- Bilingual right-to-left dashboard design and privacy-aware masking and de-identification practices.
Preferred qualifications
- Arabic NLP; Vertex AI or BigQuery ML.
- Marketing-mix, attribution, or uplift modeling.
- Qlik or Power BI certification; Google Professional Machine Learning Engineer certification.
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