FORECASTING AND TIME SERIES ANALYSIS-CONSULTANT

CGIAR System Organization; Montpellier; Sur place; EUR 60 000 - 80 000; Plein temps

Résumé du poste

A not-for-profit international agricultural research organization is looking for a Consultant specialized in forecasting and time series analysis. The role involves using Machine Learning and Artificial Intelligence techniques to enhance seed requirement estimations for vegetatively propagated crops. This consultancy is limited to Kenyan and Ugandan nationals and will last up to 30 days.

Qualifications

• PhD in Agribusiness, Agricultural Economics, or Applied Econometrics.
• Minimum of 8 years relevant experience in time series analysis.
• Excellent English writing and data management skills.

Responsabilités

• Conduct systematic review for demand estimation.
• Predict area under cultivation and yield using ML and AI.
• Prepare technical reports and collaborate on SRE tool.

Connaissances

Forecasting

Time series analysis

Machine Learning

Artificial Intelligence

Data management

Data analysis

Formation

PhD in Agribusiness or related fields

Description du poste

FORECASTING AND TIME SERIES ANALYSIS - CONSULTANT

CIP is a not-for-profit international agricultural research organization with a global mandate to conduct research on potatoes, sweetpotatoes, Andean root and tuber crops, and sustainable management of natural resources. CIP’s vision is to contribute from its areas of expertise to the fulfillment of the Millennium Development Goals (MDGs), particularly those related to poverty, hunger, child and maternal mortality, and sustainable development. CIP has its headquarters in Lima, Peru, with staff and activities across Africa, Asia, and Latin America. CIP is a member of the One CGIAR, a global research partnership aimed at transforming food, land, and water systems in a climate crisis, involving 13 CGIAR Centers/Alliances in collaboration with numerous partners including research institutes, civil society, academia, development organizations, and the private sector.

Background

We have developed the Seed Requirement Estimation (SRE) tool to estimate seed needs at each stage of the seed value chain (https://mt.co.ug/bid_tools). Currently, the tool relies on assumptions to estimate seed requirements, which depend heavily on the area under cultivation for vegetatively propagated crops (VPCs). We use a linear projection model, but it lacks the accuracy achievable with Machine Learning (ML) or Artificial Intelligence (AI). Therefore, we aim to integrate ML and AI techniques to improve predictions of area under cultivation, production, and yield for VPCs across various market segments, focusing on Uganda, Tanzania, and India, specifically for crops like potato, sweetpotato, and cassava. Additionally, we have introduced specific varieties tailored to each country's market segments.

Objective

1. Conduct a systematic review for demand estimation, with a focus on VPCs.
2. Predict area under cultivation, production (tons), and yield (tons/ha) using time series analysis, ML, and AI techniques, based on historical data and influencing factors.
3. Prepare technical reports and briefs.
4. Collaborate with the SRE digital team to integrate and validate this information within the SRE tool.
5. Prepare a manuscript for peer-reviewed publication.
6. Design study and conduct preliminary desk research.
7. Develop preliminary reports for review.
8. Deliver final reports, briefs, and manuscripts.

Requirements

Selection Criteria

• PhD in Agribusiness, Agricultural Economics, Applied Econometrics, or related fields in time series analysis.
• Minimum of eight (8) years of relevant experience.
• Knowledge of forecasting, time series analysis, ML, AI, ARIMA, ARCH, and GARCH models.
• Excellent English writing skills, data management and analysis capabilities, and ability to work with multidisciplinary teams from private and public sectors.

Time Frame

The consultancy contract will last up to 30 days.

Conditions: This is a national consultancy position limited to Kenyan and Ugandan nationals and permanent residents.