Organizational 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 (50×2030, 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. Under this light, 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.

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, particularly in Natural Language Processing (NLP), and Artificial Intelligence (AI) methods, ideally including Generative AI (GenAI), with a focus on Large Language Models (LLMs), Python programming, cloud compute providers (e.g., Google Cloud Platform (GCP), Amazon Web Services (AWS)):

•    Agricultural Data Science and Predictive Analytics: Utilizing AI methods such as machine learning models, LLMs, and data fusion techniques to analyze agricultural statistics, predict production trends, optimize trade decisions, and assess resource utilization.
•    Food Security and Nutrition Analytics: Employing NLP and LLMs to extract and analyze information from unstructured data sources, using AI for early warning systems, trend analysis, and policy evaluation in food security and nutrition.
•    Advanced AI-Driven Data Processing and Visualization: Leveraging Python, R, and other tools for AI-based data processing, predictive modeling, and dynamic visualization, integrating AI technologies to improve data insights.
•    Automated Data Collection and Text Mining Techniques: Utilizing AI and machine learning for enhanced data processing, including NLP for text mining, legal and policy documents analysis, data extraction from web and social media sources, and improving data quality through automated methods.
•    Integration of AI in Statistical Projects: Developing statistical projects that merge conventional statistical methods with cutting-edge AI techniques, such as LLMs and deep learning, to innovate data collection, processing, and analysis practices.

Reporting Lines

Consultants and PSA subscribers will work under the immediate supervision of the Senior Statistician/Methodology Innovation Team Leader, or eventually another ESS Team Leader, and the general oversight of the Chief Statistician, Director and Deputy Director of ESS. They may be called upon to collaborate with other FAO Divisions and teams.

Technical Focus 

The incumbent will develop and utilize advanced data science methods and AI techniques, with a focus on NLP and LLMs, to extract insights from large volumes of unstructured data and analyze information in agriculture, food security, and nutrition. Responsibilities include data processing, text mining, predictive modeling, and creating innovative solutions for data integration, automation, and visualization to enhance 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 statistics and data science methods, including the integration of AI and NLP techniques for innovative analyses.
•    Design and implement advanced methods, data collection processes, and analytical frameworks, utilizing a robust set of tools including R, Python, SQL and No-SQL databases, and related technologies and paradigms (e.g. machine learning, NLP, crowdsourcing, text mining, web scraping).
•    Drive the analysis, validation, and dissemination of complex datasets, with traditional statistical/data-engineering methods or employing AI and machine learning to enhance data interpretation and decision-making.
•    Utilize technologies for text mining and/or LLMs to extract insights from vast, unstructured data sets of documents.
•    Engage in statistical capacity development, providing technical assistance and training that covers both foundational statistical skills and modern data science and AI techniques.

CANDIDATES WILL BE ASSESSED AGAINST THE FOLLOWING

Minimum Requirements

•    Advanced university degree in data science, statistics, economics, computer science, or a related and relevant field.
•    At least 1 year of relevant experience in the field of data science, machine learning, natural language processing, artificial intelligence.
•    Working knowledge (level C) of English.

FAO Core Competencies

•    Results Focus
•    Teamwork
•    Communication
•    Building Effective Relationships
•    Knowledge Sharing and Continuous Improvement

Technical/Functional Skills  

•    Demonstrated proficiency and extensive experience in performing the above-mentioned tasks and responsibilities in relevant statistical or data science fields.
•    Experience in data exploration, preprocessing, and transformation techniques for handling diverse data types, including structured and unstructured formats. Experienced in a variety of machine learning classification models and clustering algorithms. Competent in evaluating and optimizing models using metrics. Knowledgeable in ETL processes and data engineering.
•    Strong foundation in deploying, fine-tuning, and customizing Large Language Models (LLM) for NLP tasks. Experienced with models such as GPT, BERT, and T5, applying them to tasks like text generation, summarization, classification and sentiment analysis. Skilled in fine-tuning LLMs on domain-specific data using frameworks like Hugging Face Transformers and TensorFlow or PyTorch. Proficient in creating Retrieval-Augmented Generation (RAG) tools.
•    Proficient in Python (with extensive use of libraries such as Pandas, NumPy, Scikit-learn, TensorFlow) and R for data manipulation, model development, and deployment. Skilled in data collection methods, including web scraping, API integration, and working with distributed data processing tools. Knowledgeable in SQL and NoSQL databases. Experienced in creating and optimizing ETL pipelines and understanding big data principles. Skilled in cloud computing platforms like GCP (preferably) or AWS.
•    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 domains and tasks would be an asset.
•    Experience in the provision of technical assistance to countries and/or professional experience in national statistical services.

Level of Education: Bachelor Degree

Work Hours: 8

Experience in Months: No requirements