Econometrics and Statistics Specialist

Organizational Setting

The Office of Emergencies and Resilience (OER) is responsible for ensuring FAO’s efforts to support countries and partners in preparing for and effectively responding to food and agricultural threats and crises. It is responsible for coordinating the development and maintenance of corporate tools and standards to enable Decentralized Offices to assist member countries to prepare for, and respond to emergencies. OER ensures humanitarian policy coordination and knowledge, liaison with the Inter-Agency Standing Committee as well as with humanitarian resource partners, co-leadership with World Food Programme of the global Food Security Cluster, organizational preparedness, surge capacity and response to large-scale emergencies. OER supports food and nutrition security assessment and early warning activities related to emergency and humanitarian analysis and responses. OER plays a major role in the development and leadership of the Organization’s programme to increase the resilience of livelihoods to food and agriculture threats and crises.

Within OER, the Evidence Team supports the implementation and development of adequate capacities at all levels, especially at country level, to collect data and conduct analysis including risk assessments to directly inform and support the design of emergency, anticipatory action and resilience activities and monitor their impact.

Since 2020, the Evidence Team has established the Data in Emergencies (DIEM) information system. Driven by regularly collected primary data in fragile and shock‑prone environments, DIEM consists of four pillars: • DIEM-Monitoring is a monitoring system of agricultural livelihoods and food security in the context of multiple shocks in over 25 food crisis countries. DIEM-Monitoring relies on periodic household and key informant data collection, through phone and in-person surveys, and analysis; • DIEM-Impact conducts ex-post assessments to understand the impacts of large-scale hazards on agricultural livelihoods and value chains, using phased methodological approaches such as remote-sensing, damage & loss analyses, Post-Disaster Needs Assessments (PDNA), and other food security & livelihood surveys. • DIEM-Risk consists of risk profiles derived from geographic baselines of past events and their impacts on agricultural livelihoods. • DIEM-Research ensures that data analysis undertaken through the DIEM programme can answer key questions relevant to risk-informed, efficient and effective programming in support of agricultural livelihoods in fragile and risk prone contexts. A research, analysis and learning agenda composed of various building blocks meant to investigate relationships between key variables over time and across space, synthetize the many DIEM indicators in a meaningful fashion (agricultural livelihood stress index, household profiling, shocks index), and expand/deepen DIEM data through machine learning techniques has been developed. The various outputs of these four pillars are processed, visualized and disseminated through the Data in Emergencies hub (https://data-in-emergencies.fao.org/).

Reporting Lines

The Econometrics and Statistics Specialist will report to the Senior Technical Officer (DIEM Team Leader). He/she will work in close collaboration with other DIEM team members, including the Survey Methods, Research & Analysis (SMAR) analysts, the DIEM Deputy Team Leader, the DIEM Data Manager, the DIEM Research Capacity and Learning advisor and Regional Assessment specialists. He/she will build linkages with other teams in OER and other divisions with FAO, in particular the Statistics Division (ESS), and with external partners (including research partners, implementing partners and WFP).

Technical Focus

The Econometrics and Statistics Specialist is responsible for technically leading the enhancement of DIEM analytical capacities, in relation to the Research, Analysis and Learning agenda. He/she will spearhead research and development to enhance and advance DIEM products, and act as the outward-facing interface for DIEM on econometrics and advanced analytics. The processes and products generated will be relevant to the Emergency and Resilience Programming of FAO as well as the activities of other stakeholders including the global Food Security Cluster, Research partners and resource partners.

Tasks and responsibilities

Analysis: • Apply advanced statistical techniques to analyse large datasets, identifying relationships, causalities, profiles, trends, and insights for decision-making; • Apply data reduction techniques to distil large datasets into meaningful insights without losing critical information; • Build and validate statistical models for time series analysis including pattern identification, seasonality analysis and forecasting; • Perform spatial data analysis to understand geographical trends; • Design and implement data analysis workflows using R and/or Python to be incorporated into existing scripts; • Lead on meta-analyses of multi-round, multi-country household data, from the analysis plan to reporting;

Technical support and training: • Support regional and country analysts in the formulation and framing of research and analytical questions; • Assist the SMAR team by providing technical inputs in the development of tools and training materials to be used by regional and country analysts, in view of improving the analytical depth of DIEM products; • Collaborate with DIEM Regional Advisers (RAs) and the DIEM SMAR team to understand data requirements and provide support on complex sample design; • Continuously explore and integrate new data sources techniques to enhance the analytical capabilities of the DIEM team;

Visualization, reports and documentation: • Facilitate data-driven decision-making by providing clear and concise visual summaries of analysis. Work with RAs, the DIEM Communications team and the DIEM Hub team to translate complex analytical findings into understandable and actionable visual reports; • Provide technical inputs to the development of an annual DIEM global report and lead the development of specific analytical papers and contributions to FAO flagship publications based on DIEM; • Document methodologies and findings, ensuring transparency, accessibility and easy replicability of models; • Participate in the development of journal articles for Peer Reviewed journals

DIEM-Risk and DIEM-MEAL: • Peer review and propose improvements to the risk model structure and equation, including risk dimensions, ingredients, indexes, normalization of variables and weights under DIEM-Risk; • Provide technical inputs to the implementation of integrated DIEM and MEAL studies;

Partnerships: • Act as the leading technical interface between DIEM team (OER) and partner universities (including Cornell and Tulane Universities), research centers (including IFPRI), WFP, DataLab (FAO-ESS, DIEM’s key partner on machine learning), and other internal and external partners as needed;

Other: • Stay abreast of the latest developments in statistical analysis and relevant technologies; • Undertake travel, as required; • Perform any other duties, as required.

CANDIDATES WILL BE ASSESSED AGAINST THE FOLLOWING

Minimum Requirements

• Advanced degree in Quantitative Economics/Development Economics/Agricultural Economics, Econometrics or Applied Statistics. • At least 5 years of relevant experience in analyzing large, complex agricultural/ food security related data sets, preferably including data from food crisis/ emergency/protracted crisis contexts. • For consultants: working knowledge of English (C-level) and limited knowledge (В-level) of one of the other official languages of the organization (French, Spanish, Chinese, Russian or Arabic). For PSA: Working knowledge of English (C-level).

FAO Core Competencies

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

Technical/Functional Skills

• Expert level competence in at least one and preferably two of the following: SPSS, STATA, R; • Ability to analyse large cross-sectional and time-series datasets; • Ability to work under pressure and adapt to an evolving and complex humanitarian context and within multidisciplinary and different cultural background teams; • Ability to demonstrate creativity while complying with internal standard procedures and practices; • Excellent communication skills, both verbal and written; • Ability to communicate with technical and non-technical people; • Effective team leadership and decision making skills with strong individual planning capacity.

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Added 14 days ago - Updated 7 hours ago - Source: fao.org