Data Scientist

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.

Reporting Lines

Consultants and PSA subscribers will work under the immediate supervision of one or more Team Leaders of ESS, and the general oversight of Office 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

We are seeking consultants and PSA subscribers with expertise in one or more of the following areas, with a focus on data science techniques, particularly in Natural Language Processing (NLP) and Artificial Intelligence (AI), including Generative AI and Large Language Models (LLMs), as well as Python programming and major cloud computing platforms (e.g. GCP, AWS, Azure): 
•     Agricultural Data Science and Predictive Analytics: Utilizing AI methods such as machine learning models, LLMs and data fusion techniques to agricultural and food security statistics, including production analysis, trade-related insights, and the integration of AI methods within conventional statistical workflows. 
•     Food Security and Nutrition Analytics: Employing NLP and LLMs to extract and analyze information from unstructured data sources, including documents and web-based content, 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 and content extraction from web and social media sources, automatic classification and building of data-driven taxonomies, 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, portfolio analysis, synthesis, and management, and analysis practices. 
•     The work requires addressing complex analytical questions involving heterogeneous data sources, varying data quality, and evolving analytical requirements. Particular attention is required to methodological rigor, transparency of assumptions, and careful interpretation of results.

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 NoSQL databases, and related technologies and paradigms (e.g., machine learning, NLP, text mining, web data extraction). 
•     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 and semantics from vast, unstructured data sets of documents. 
•     Translate analytical and policy-relevant questions into appropriate statistical and data science approaches, ensuring methodological soundness and transparency. 
•     Critically assess analytical results and model outputs, including limitations, assumptions, and potential sources of bias. 
•     Prepare clear analytical narratives to communicate findings, uncertainties, and methodological choices to non-technical audiences. 
•     Experience in assessing model performance, robustness, and risks, including validation of outputs and appropriate use of AI-generated content in analytical contexts. 
•     Ensure reproducibility, documentation, and quality control of analytical workflows and outputs. 
•     Collaborate with subject-matter experts to ensure that analytical approaches are grounded in domain knowledge and fit for purpose. 
•     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 from an institution recognized by the International Association of Universities (IAU)/UNESCO in data science, statistics, economics, computer science or any other discipline relevant to the work of the Organization. Consultants with bachelor's degree need two additional years of relevant professional experience. 
•     At least 1 year of relevant experience in the field of data science, machine learning, natural language processing, artificial intelligence or any other of the above-mentioned areas of work and/or fields of application. 
•     Working knowledge of English (level C).

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 like text, images, and time-series data. Proficient in statistical analysis and feature engineering to create informative predictors and enhance model performance. 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, translation, 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 Retrieval-Augmented Generation (RAG) models.  
•     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 like Spark, Hadoop, and Dask. Knowledgeable in SQL and NoSQL databases for data storage and querying. Experienced in creating and optimizing ETL pipelines and understanding big data principles for handling and processing large-scale datasets. Skilled in cloud computing platforms like GCP (preferably), AWS, or Azure for scalable data science solutions. 
•     Knowledge of a second FAO language will be considered an asset.
•     Ability to draft quickly, clearly and concisely and to communicate effectively in English. 
•     Ability to work with a high degree of autonomy in complex analytical assignments, while coordinating effectively with technical and non-technical stakeholders. 
•     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.