Machine Learning Engineer

Organizational Context

The Decision-Support and Data Science Division (DSDSD) within ESCWA is modernizing and innovating to provide advanced analytics and decision-support services. Aligned with the UN 2.0 agenda, DSDSD uses data-driven insights and emerging technologies for strategic foresight and policymaking. This role supports the Computational Economics Unit, leveraging data to inform regional development analysis.

Job Purpose

The Machine Learning Engineer will support the Computational Economics Unit by designing, developing, and deploying machine learning and computational economics methods for socioeconomic data analysis in the Arab region. This role is crucial for enhancing the analytical depth of the Arab Development Portal and its ISPAR platform. The engineer will bridge quantitative methods with economic reasoning to inform regional development strategies and policymaking. A key focus is applying advanced computational economics techniques to generate evidence-based insights from macroeconomic and indicator datasets, contributing to strategic foresight and operational efficiency within ESCWA and Member States.

Responsibilities

Develop, train, and validate supervised and unsupervised machine learning models for socioeconomic datasets, focusing on applications like nowcasting, forecasting, optimization, anomaly detection, and structural change analysis. Implement ETL pipelines for data ingestion and transformation from diverse sources, and maintain reproducible ML experiments using version-controlled pipelines. Translate economic research questions into quantitative models, conduct statistical and econometric analyses to validate outputs, and collaborate with economists to align ML design with economic theory. Implement RESTful APIs for model integration, ensuring scalability and maintainability. Prepare technical reports and documentation for both technical and non-technical audiences, and collaborate with cross-functional teams on data strategies.

Work Experience

A minimum of 5 years of professional experience in machine learning engineering or a related discipline is required. Demonstrated proficiency in Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow) and experience applying ML/statistical methods to structured tabular and time-series data are essential. Familiarity with causal inference, MLOps tools, and econometric methods is desirable.

Skills

Machine Learning Model Development, Computational Economics Methods, ETL Pipeline Implementation, Data Ingestion and Transformation, Reproducible ML Experiments, Statistical and Econometric Analysis, API Integration, Technical Documentation, Collaboration, Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow), Time-Series Modelling, Optimization, Anomaly Detection, Causal Inference (desirable), MLOps (desirable), Econometric Methods (desirable).

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