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• FAO is committed to achieving workforce diversity in terms of gender, nationality, background and culture.
• Qualified female applicants, qualified nationals of non-and under-represented Members and person with disabilities are encouraged to apply;
• Everyone who works for FAO is required to adhere to the highest standards of integrity and professional conduct, and to uphold FAO's values
• FAO, as a Specialized Agency of the United Nations, has a zero-tolerance policy for conduct that is incompatible with its status, objectives and mandate, including sexual exploitation and abuse, sexual harassment, abuse of authority and discrimination　　
• All selected candidates will undergo rigorous reference and background checks
• All applications will be treated with the strictest confidentiality

FAO’s commitment to environmental sustainability is integral to our strategic objectives and operations.

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 (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. 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.

Please note that all candidates should adhere to FAO Values of Commitment to FAO, Respect for All and Integrity and Transparency

ADDITIONAL INFORMATION

• FAO does not charge any fee at any stage of the recruitment process (application, interview, processing)
• Please note that FAO will only consider academic credentials or degrees obtained from an educational institution recognized in the IAU/UNESCO list
• Please note that FAO only considers higher educational qualifications obtained from an institution accredited/recognized in the World Higher Education Database (WHED), a list updated by the International Association of Universities (IAU) / United Nations Educational, Scientific and Cultural Organization (UNESCO). The list can be accessed at http://www.whed.net/ 
• For more information, visit the FAO employment website
• Appointment will be subject to certification that the candidate is medically fit for appointment, accreditation, any residency or visa requirements, and security clearances.

HOW TO APPLY

• To apply, visit the recruitment website at Jobs at FAO and complete your online profile. We strongly recommend that your profile is accurate, complete and includes your employment records, academic qualifications, and language skills
• Candidates are requested to attach a letter of motivation to the online profile
• Once your profile is completed, please apply, and submit your application
• Candidates may be requested to provide performance assessments and authorization to conduct verification checks of past and present work, character, education, military and police records to ascertain any and all information which may be pertinent to the employment qualifications
• Incomplete applications will not be considered
• Personal information provided on your application may be shared within FAO and with other companies acting on FAO’s behalf to provide employment support services such as pre-screening of applications, assessment tests, background checks and other related services. You will be asked to provide your consent before submitting your application. You may withdraw consent at any time, by withdrawing your application, in such case FAO will no longer be able to consider your application
• Only applications received through the FAO recruitment portal will be considered
• Your application will be screened based on the information provided in your online profile
•  We encourage applicants to submit the application well before the deadline date.

If you need help or have queries, please create a one-time registration with FAO’s client support team for further assistance: https://fao.service-now.com/csp

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