Agriculture Specialist

buscojobs Brasil; Sergipe; Teletrabalho; BRL 80.000 - 120.000; Tempo parcial

Resumo da oferta

A recruitment agency in Brazil is seeking an Image Annotation Analyst to work on agricultural datasets. The ideal candidate will have a degree in agriculture and experience in data annotation, focusing on crop classification and growth stages. The project starts this week with flexible hours at an hourly rate of $16.

Qualificações

• Experience in agricultural field studies and data annotation.
• Prior work with street view imagery of agricultural datasets.

Responsabilidades

• Annotate agriculture dataset and identify crop type and stages.
• Ensure quality, consistency, and accuracy of datasets.
• Interpret crop types and identify growth stages.

Conhecimentos

Data annotation

Crop classification

Knowledge of phenology

Field boundary identification

Formação académica

Bachelor’s / master’s degree in agriculture

Descrição da oferta de emprego

We are looking for Image Annotation Analyst having background in Agriculture from Brazil. If you are interested and reside in Brazil, kindly apply to the post on urgent basis as we are anticipating the project to get started this week.

Please find the details below:

• Project Name: Image Annotation Analyst- Agriculture
• Hourly Rate: $16 per hour
• Required Hours: 5-6 hours per day (flexible hours)
• Project Start Date: This week
• Location: Brazil
• Language: local
• Required Education: Agriculture or agriculture related streams like horticulture, food science etc.

We are looking for an Annotation Analyst to join the team. The ideal candidate will be responsible for annotating agriculture dataset, identifying crop type and stages, ensuring the quality, consistency, and accuracy of datasets. Raters are required to interpret crop types, annotation, and identifying the stages.

Required Skills & Qualifications

• Bachelor’s / master’s degree in agriculture
• Experience in agricultural field studies and data annotation.
• Knowledge of crop classification, phenology (growth stages), and field boundary identification.
• Prior work with street view imagery of agricultural datasets.