Machine Learning Engineer

Job Posting Organization:
The position is located within the Decision-Support and Data Science Division (DSDSD) of the United Nations Economic and Social Commission for Western Asia (ESCWA). ESCWA was established to promote economic and social development in the Arab region. The organization focuses on providing advanced analytics and decision-support services to its member states and other UN entities. The DSDSD is part of ESCWA's modernization and innovation efforts, leveraging data-driven insights and emerging technologies to inform policymaking and operations. The division employs a range of functions including data integration, quality assurance, advanced analytics, machine learning, and the development of digital decision-support tools. Through these capabilities, ESCWA aims to empower evidence-based decision-making and foster strategic innovation across the region.

Job Overview:
The Machine Learning Engineer will play a crucial role in supporting the Computational Economics Unit by designing, developing, and deploying machine learning and computational economics methods applied to socioeconomic data specifically focused on the Arab region. This position emphasizes the integration of quantitative methods with economic reasoning to enhance regional development analysis and policymaking. The engineer will work autonomously on model design and implementation, adhering to methodological frameworks established with the unit lead. The role requires a strong foundation in computational economics, econometric modeling, time-series analysis, and optimization techniques to derive insights from the unit's datasets. The engineer will also contribute to the analytical depth of the Arab Development Portal and its ISPAR platform, ensuring that the methodologies employed are robust and aligned with the overarching goals of the organization.

Duties and Responsibilities:
The Machine Learning Engineer will be responsible for a variety of tasks including:

• Machine Learning Model Development: Designing, training, and validating both supervised and unsupervised machine learning models on socioeconomic datasets, focusing on applications such as nowcasting, forecasting, optimization, anomaly detection, and structural change analysis. The engineer will also apply computational economics methods, including agent-based modeling and computable general equilibrium frameworks, and will implement ETL pipelines for data ingestion and transformation.
• Economic Data Analysis and Modeling: Translating economic research questions into quantitative models that utilize large-scale structured and semi-structured datasets. Conducting statistical and econometric analyses to validate model outputs and provide confidence intervals and scenario projections.
• API Integration and Technical Infrastructure: Implementing RESTful APIs to expose model outputs and ensuring model scalability and maintainability.
• Collaboration and Reporting: Working with cross-functional teams to align data strategies and analytical outputs, and preparing technical reports and presentations to communicate methodologies and findings effectively to both technical and non-technical audiences.

Required Qualifications:
Candidates must possess a bachelor's degree in computer science, data science, applied mathematics, economics, or a related field. A master's degree in a similar field is desirable. All applicants are required to submit a copy of their educational degree, as incomplete applications will not be considered. Additionally, a minimum of 5 years of professional experience in machine learning engineering or a closely related discipline is required. Proficiency in Python, particularly with libraries such as NumPy, pandas, scikit-learn, and either PyTorch or TensorFlow, is essential. Candidates should also have demonstrated experience applying machine learning and statistical methods to structured tabular and time-series data, including time-series modeling and forecasting. Familiarity with econometric methods and causal inference techniques is desirable.

Educational Background:
The educational background required for this position includes a bachelor's degree in computer science, data science, applied mathematics, economics, or a related field. A master's degree in one of these areas is considered an asset. Candidates must provide proof of their educational qualifications as part of the application process, as incomplete applications will not be reviewed.

Experience:
The position requires a minimum of 5 years of professional experience in machine learning engineering or a closely related field. Candidates should have a strong track record of applying machine learning techniques and statistical methods to real-world datasets, particularly in the context of socioeconomic data. Experience with MLOps tools and pipeline orchestration is also desirable, as is familiarity with econometric methods and statistical inference.

Languages:
Fluency in English is required for this position, with a rating of 'fluent' in all four areas: speaking, reading, writing, and understanding. Knowledge of French is beneficial, as it is one of the working languages of the United Nations Secretariat. Additionally, Arabic is a working language of ESCWA, and knowledge of Arabic is advantageous but not mandatory.

Additional Notes:
The position is remote and is expected to last for a duration of 6 months. There are no fees associated with the application process, and the United Nations does not charge candidates at any stage of recruitment. The organization emphasizes that it does not require information regarding applicants' bank accounts.