Remote Agricultural Data Scientist: Yield & Farm Optimization

Placements24; East London; Remote; ZAR 600,000 - 900,000; Full time

Job summary

Placements24 is seeking an Agricultural Data Scientist to transform vast agricultural data into actionable insights. The role is remote, supporting decision-making for farm operations, crop productivity, and sustainability by leveraging data from sensors, drones, and satellite imagery.

The ideal candidate will have a strong background in statistical modeling, machine learning, and data visualization, and will collaborate with agronomists to define KPIs and create data-driven recommendations

Qualifications

• Strong background in statistical modeling, machine learning, and data visualization.

Responsibilities

• Collect, clean, and preprocess large datasets from diverse agricultural sources.
• Develop and apply statistical models and machine learning algorithms to analyze agricultural data, identify patterns, and predict trends (e.g., yield prediction, disease outbreak forecasting).
• Design and implement experiments to test hypotheses and validate models.
• Create compelling data visualizations and dashboards to communicate complex findings to technical and non-technical stakeholders.
• Collaborate with agronomists and domain experts to define key performance indicators (KPIs) and analytical requirements.
• Develop data-driven recommendations for optimizing farming practices, resource allocation, and crop management strategies.
• Contribute to the development of new data products and analytical tools for the agricultural industry.
• Stay current with the latest advancements in data science, machine learning, and their applications in agriculture.
• Ensure data integrity, security, and compliance with relevant privacy regulations.
• Work independently and effectively within a remote team structure.

Job description

What Agricultural Data Scientist Yield Farm Op...

About the RoleOur client, a forward-thinking agricultural technology firm, is seeking a highly analytical and innovative Agricultural Data Scientist to join their team. This position is entirely remote, allowing you to contribute your expertise from anywhere. You will be responsible for transforming vast amounts of agricultural data into actionable insights that drive decision-making, optimize farm operations, and enhance crop productivity. This includes working with data from sensors, drones, satellite imagery, and farm management systems. The ideal candidate possesses a strong background in statistical modeling, machine learning, and data visualization, coupled with a keen understanding of agricultural processes. You will collaborate closely with agronomists, farmers, and product development teams to identify key trends, predict outcomes, and develop data-driven solutions to complex challenges in the agricultural sector. Your remote role will be crucial in advancing our client's mission to make farming more efficient, sustainable, and profitable.

Key Responsibilities

• Collect, clean, and preprocess large datasets from diverse agricultural sources.
• Develop and apply statistical models and machine learning algorithms to analyze agricultural data, identify patterns, and predict trends (e.g., yield prediction, disease outbreak forecasting).
• Design and implement experiments to test hypotheses and validate models.
• Create compelling data visualizations and dashboards to communicate complex findings to technical and non-technical stakeholders.
• Collaborate with agronomists and domain experts to define key performance indicators (KPIs) and analytical requirements.
• Develop data-driven recommendations for optimizing farming practices, resource allocation, and crop management strategies.
• Contribute to the development of new data products and analytical tools for the agricultural industry.
• Stay current with the latest advancements in data science, machine learning, and their applications in agriculture.
• Ensure data integrity, security, and compliance with relevant privacy regulations.
• Work independently and effectively within a remote team structure.

Requirements