IndirizzoOrganizational Setting
The Statistics Division (ESS) develops and advocates for the implementation of methodologies and standards for data collection, validation, processing and analysis of food and agriculture statistics. In these statistical domains, it also plays a vital role in the compilation, processing and dissemination of internationally comparable data, and provides essential capacity building support to member countries. In addition, the Division disseminates many publications, working papers and statistical yearbooks, which cover agricultural and food security relevant statistics (including prices, production, trade and agri-environmental statistical data). The Statistics Division is involved in the management of a number of large-scale projects (50x2030, Global Strategy, FIES) aimed at improving statistical methodologies and establish best practices for the collection, collation, processing, dissemination and use of data relevant to food security, agriculture and rural areas.
Rapid technological development requires ESS to innovate in a variety of areas to modernize the statistical business process and meet the increasingly demanding needs for timely, accurate, and cost-effective data and analysis. Therefore, it is part of FAO’s strategy to engage with non-official, non-conventional, Big Data sources and to rely on data science and Artificial Intelligence methods to solve the current information gaps problems. The final objective is to expand the quantity, quality and range of the statistical and analytical products of the division. In this context, a Data Lab for Statistical Innovation has been established to lead the Division’s work related to modern data science applications with the objective to solve research problems in agriculture statistics and policy (e.g. policy analysis, use of statistics in policy making) by leveraging existing information.
Reporting Lines
Consultants and PSA subscribers will work under the immediate supervision of the Methodological Innovation Team Leader, or eventually another ESS Team Leader, and the general oversight of Director and Deputy Director of ESS. They may be called upon to collaborate with other FAO Divisions and teams.
Technical Focus
We are seeking consultants and PSA subscribers with expertise in one or preferably more of the following areas, with focus on cutting-edge data science techniques, Natural Language Processing (NLP), Artificial Intelligence (AI), ideally including Large Language Models and/or Geospatial-AI:
• Sustainable Development Goals monitoring and indicators through advanced document analysis, information extraction, and big data analytics.
• Agricultural statistics, including production, trade, and utilization, enriched with predictive models and analyses.
• Food security, nutrition statistics, and other agricultural domains, leveraging advanced text analysis methods and large language models to extract actionable insights from unstructured data sources.
• Climate change, socio-economic, and environmental statistics, disasters loss and damage assessment, applying machine learning models, spatial models, and AI methods to address complex challenges in these fields.
• Advanced application of statistical/technical software languages like R and Python for programming, implementation and optimization of algorithms, data processing, analysis, and visualization.
• Technologies and methods for data collection, processing, and text mining, including the use of AI and machine learning for enhancing data management, quality and insights.
• Development and implementation of statistical projects that integrate conventional statistical methods with cutting-edge data science technologies and non-conventional sources (social media, web scraped and/or crowdsourced data, cellphone records, nightlights).
• GIS and satellite imagery application to obtain land use classification, feature extraction, object detection, and instance segmentation, preferably by applying AI methodologies to these tasks.
• Development of geospatial methods with the aim of obtaining Gross Domestic Product, sectoral value added, other socio-economic variables and other type of detailed geographical insights (e.g., displacement, aid development) at sub-national (very granular) and/or high frequency level, for contributing to a comprehensive understanding of regional dynamics for informed decision-making.
Tasks and responsibilities
In one or more of the above-mentioned statistical domains, Consultants and PSA subscribers will contribute to and/or take responsibility for one or more of the following tasks:
• Contribute to methodological development in data science methods, including the integration of AI and NLP techniques for innovative analyses.
• Design and implement data collection processes from non-conventional sources utilizing a robust set of tools including R, Python, SQL and No-SQL databases, and related technologies and paradigms (e.g., remote sensing, crowdsourcing, text mining, web scraping).
• Drive the analysis, validation, and dissemination of complex datasets, with data engineering methods and Artificial Intelligence to enhance data interpretation and decision-making.
• Utilize advanced algorithms and natural language processing techniques for text mining and/or Large Language Models to extract information from vast and unstructured data sets of documents to uncover hidden knowledge, enhance information retrieval, and contribute to a deeper understanding of complex narratives contained within extensive textual corpora.
• Develop and implement geospatial models to assess the impact of various factors on agricultural productivity, land use, and other relevant indicators, incorporating satellite imagery, GIS (Geographic Information System), and remote sensing technologies.
• Lead or assist in the creation, development, oversight, and innovation of statistical projects, aligning them with the latest advances in data science and technology.
• Engage in statistical capacity development, providing technical assistance and training that covers modern data science / Artificial Intelligence techniques.
• Support implementation of statistical policies that embrace innovative approaches to data analysis, with an emphasis on big data and non-conventional data sources, ensuring quality and relevance in the evolving landscape of agricultural and rural statistics.
CANDIDATES WILL BE ASSESSED AGAINST THE FOLLOWING
Minimum Requirements
• University degree in statistics, economics, social sciences, computer science, data science, or a related and relevant field.
• At least one year of relevant experience in the above-mentioned areas of work and/or fields of application.
• For Consultants: Working knowledge of English, French or Spanish and limited knowledge of one of the other two or Arabic, Chinese, Russian. For PSAs: Working knowledge of one of the FAO languages.
FAO Core Competencies
• Results Focus
• Teamwork
• Communication
• Building Effective Relationships
• Knowledge Sharing and Continuous Improvement
Technical/Functional Skills
• Work experience in more than one location or area of work
• Extent and relevance of experience in performing the above-mentioned tasks and responsibilities in relevant data science and/or Artificial Intelligence fields.
• Capacity to stay current with technical advancements in data science and AI, including a proactive approach to embracing future innovations in these fields.
• Strong command of data science and AI techniques including: natural language processing (NLP) and AI methods for text data analysis and language modeling; exploration, preprocessing and visualization of big data; handling complex structured and unstructured data; familiarity with a range of machine learning models such as decision trees, support vector machines; familiarity with advanced deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs); expertise in transfer learning and reinforcement learning techniques; expertise in applying probability distributions and statistical principles within the context of predictive modeling and machine learning; proficiency in data manipulation and analysis with programming languages like Python or R, and tools such as Pandas, scikit-learn, NumPy, PyTorch, Keras, or TensorFlow; experienced in applying advanced dimensionality reduction techniques such as t-SNE and UMAP for feature extraction and visualization; knowledge in designing and implementing complex data collection strategies, including web scraping, API calls, and utilizing big data platforms like Hadoop or Spark for distributed data processing; expertise in model evaluation metrics and methods, including cross-validation, A/B testing, ROC curves, and confusion matrices; use of geospatial methods, applying GIS tools and methodologies for spatial analysis.
• Extent of practical knowledge of deploying and managing projects on major cloud platforms like Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure. This includes understanding how to leverage these platforms, and containerization and orchestration tools like Docker and Kubernetes, for scalable data processing, machine learning model training, and deployment.
• Extent of experience with the Git version control systems for collaborative development and code management.
• Ability to draft quickly, clearly and concisely and to communicate effectively in English.
• Ability to work independently, with minimum supervision.
• Previous working experience with FAO and its partners in the above-mentioned statistical domains and tasks would be an asset.
• Experience in the provision of technical assistance to countries and/or professional experience in national statistical services would be an asset.
