Machine Learning Engineer

The objective of the Individual Contractor (IC) is to support the Computational Economics Unit of the Decision-Support and Data Science Division by designing, developing, and deploying machine learning and computational economics methods applied to socioeconomic data on the Arab region. The IC will contribute to the analytical depth of the Arab Development Portal and its underlying ISPAR platform, with a particular focus on bridging quantitative methods and economic reasoning to inform regional development analysis and policymaking. A central emphasis is placed on computational economics, drawing on econometric modelling, time-series analysis, optimization, network and dependency methods, and structural change detection to generate evidence-based insights from the unit's indicator and macroeconomic datasets. The IC will work with autonomy on model design and implementation, within methodological frameworks agreed with the unit lead.

Background This position is located in the Decision-Support and Data Science Division (DSDSD). The Division is part of ESCWA's broader modernization and innovation efforts, providing advanced analytics and decision-support services within ESCWA, other UN entities, and Member States. Aligned with the UN 2.0 agenda and grounded in strategic foresight, DSDSD leverages data-driven insights, emerging technologies, and scenario-based planning to anticipate trends and proactively inform policymaking and operations. Its core functions include data integration and management; data quality assurance; advanced analytics and modeling; machine learning and artificial intelligence solutions; real-time dashboards and performance reporting; and data visualization and business intelligence; and the design and development of specialized digital decision-support tools. Through these capabilities, DSDSD empowers evidence-based decision-making, fosters organizational efficiency, and catalyzes strategic innovation across the region. Tasks and Responsibilities: The Machine Learning Engineer will be responsible for the following tasks: 1. Machine Learning Model Development • Design, train, and validate supervised and unsupervised ML models on socioeconomic datasets, with applications in nowcasting, forecasting, optimization, anomaly detection, and structural change analysis. • Apply computational economics methods such as agent-based modeling, computable general equilibrium (CGE) frameworks, microsimulation techniques, and other quantitative approaches relevant to the unit's projects. Implement ETL pipelines for data ingestion, transformation, and integration from multiple sources including national statistics, UN databases, and open data platforms. • Develop and maintain reproducible ML experiments using version-controlled pipelines (MLflow equivalent). 2. Economic Data Analysis and Modeling • Translate economic research questions into quantitative models that leverage large-scale structured and semi-structured datasets. • Conduct statistical and econometric analyses to validate model outputs and provide confidence intervals, sensitivity analyses, and scenario projections. • Collaborate with economists and data scientists to align ML model design with economic theory and regional development frameworks. 3. API Integration and Technical Infrastructure • Implement RESTful APIs to expose model outputs and integrate with the different ecosystems. • Ensure model scalability, maintainability, and documentation for handoff and long-term institutional use. 4. Collaboration and Reporting • Collaborate with cross-functional teams including economists, data scientists, and software developers to align data strategies and analytical outputs. • Prepare technical reports, presentations, and documentation explaining methodologies, findings, and model performance to both technical and non-technical audiences.

A bachelor's degree in computer science, data science, applied mathematics, economics, or a related field is required. A master's degree in computer science, data science, applied mathematics, economics, or a related field is desirable. All candidates must submit a copy of the required educational degree. Incomplete applications will not be reviewed. A minimum of 5 years of professional experience in machine learning engineering or a closely related discipline is required. Demonstrated proficiency in Python (NumPy, pandas, scikit-learn, PyTorch or TensorFlow) is required.Demonstrated experience applying machine learning and statistical methods to structured tabular and time-series data, including time-series modelling, clustering, optimization, or forecasting, is required. Familiarity with causal inference methods (e.g., graphical models, causal discovery algorithms) is desirable. Experience with MLOps tools, pipeline orchestration (Airflow, Prefect, or equivalent), and experiment tracking is desirable. Familiarity with econometric methods and statistical inference is desirable. Knowledge of LLMs and their application to economic or social datasets is is desirable.

English and French are the working languages of the United Nations Secretariat; and Arabic is a working language of ESCWA. For this position, fluency in English is required. Note: “Fluency” equals a rating of ‘fluent’ in all four areas (speak, read, write, and understand) and “Knowledge of” equals a rating of ‘confident’ in two of the four areas.

Not available.