Climate Data Analyst
Organizational Context
The Food and Agriculture Organization of the United Nations (FAO) is a specialized UN agency dedicated to eradicating hunger and ensuring food security globally. Operating in over 130 countries, FAO supports national development priorities in agriculture, food security, nutrition, and resilience. In Sri Lanka, FAO has been active since 1979, focusing on transforming agri-food systems for greater efficiency, inclusivity, resilience, and sustainability. Recognizing a critical gap in operational drought prediction, FAO Sri Lanka is developing an advanced drought prediction system to support anticipatory action, aiming to reduce agricultural and livelihood losses.
Job Purpose
The Climate Data Analyst will support FAO in developing, testing, and validating trigger methodologies and impact-based forecasting approaches for Anticipatory Action in Sri Lanka. This role is crucial for establishing reliable, science-based drought forecasting with adequate lead time to enable proactive measures. The analyst will contribute to standardizing related processes and developing essential materials, tools, and knowledge products. These outputs will facilitate the mainstreaming, coordination, and dissemination of anticipatory action approaches, ultimately enhancing the country's resilience to drought and minimizing its impact on agriculture and livelihoods.
Responsibilities
The Climate Data Analyst will be responsible for reviewing, compiling, cleaning, and harmonizing diverse datasets, including rainfall, climate, vegetation, soil moisture, oceanic, forecast, and historical drought data, ensuring data quality and readiness for analysis. The role involves providing technical support for exploratory data analysis, such as correlation, lag correlation, seasonality, and cross-correlation analyses, to identify key predictors for drought forecasting. The analyst will develop, train, optimize, and validate ARIMAX models for generating drought forecasts and compare AI/ML models like Random Forest and Artificial Neural Networks for predicting drought severity and occurrence. Furthermore, the position requires facilitating the integration of model outputs into a web portal, automating monthly model updates, and preparing comprehensive documentation, including methodology notes, validation reports, source code documentation, user manuals, and operational guidelines.
Work Experience
A minimum of 4 years of relevant professional experience in the public or private sector is required, with a focus on statistical analysis, climate data analysis, drought modelling, predictive modelling, or data science. Experience in developing and validating predictive models, particularly for climate-related phenomena, is essential. Familiarity with data handling, cleaning, and harmonization for complex datasets is also crucial for this role.
Skills
Essential skills include proficiency in programming and statistical software such as R, Python, SAS, and SQL, along with relevant AI/ML tools. Demonstrated technical expertise in statistical analysis, data visualization, data modelling, data mining, predictive modelling, spatial visualization, artificial intelligence/machine learning, and statistical advisory support is required. Strong computer skills (MS Word, Excel, PowerPoint, Internet) are necessary. Experience in preparing reports and presenting information visually is highly valued. Understanding of FAO policies and programmes is considered an asset.