Remote Agricultural Data Scientist: Drive Yield with AI

Placements24; Upington; Hybrid; ZAR 900,000 - 1,300,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

Fully remote work arrangement

Professional development opportunities

Conference attendance

Job summary

Placements24 is seeking a highly analytical Remote Agricultural Data Scientist to join a forward-thinking team. You will leverage data analysis, machine learning, and statistical modeling to drive innovation in agriculture from a remote location.

Responsibilities include collecting, processing, and interpreting vast agricultural data from sensors, satellite imagery, weather stations, and farm management systems.

Qualifications

• Master's or PhD in Data Science, Statistics, Computer Science, Agricultural Science with a data focus, or related quantitative field.
• Minimum of 5 years of experience as a Data Scientist with agricultural applications.

Responsibilities

• Develop and implement advanced statistical models and ML algorithms for agricultural data.
• Extract, clean, and transform large datasets from sensors, satellite imagery, weather stations, and farm systems.
• Build predictive models for crop yield forecasting, disease detection, and resource optimization.
• Collaborate with agronomists and farm managers to understand data needs and deliver solutions.
• Design experiments to test hypotheses and validate models.
• Ensure data quality, integrity, and security throughout analysis.

Skills

Python

R

Data analysis

Statistical modeling

Machine learning

Communication

Education

Master's or PhD in Data Science/Statistics/CS/Agri-Science with data focus

Tools

SQL

NoSQL

Spark

Hadoop

GIS/Remote Sensing

Job description

Placements24 is seeking a highly analytical Remote Agricultural Data Scientist to join a forward-thinking team. You will leverage data analysis, machine learning, and statistical modeling to drive innovation in agriculture from a remote location.

Responsibilities include collecting, processing, and interpreting vast agricultural data from sensors, satellite imagery, weather stations, and farm management systems.