Agricultural Data Scientist: Supply Chain Lead

Placements24; Durban; On-site; ZAR 900,000 - 1,600,000; Full time

Benefits offered by this job

Health insurance

Retirement contributions

Performance-based bonuses

Professional development and training

Job summary

Placements24 in Durban, KwaZulu-Natal, is seeking an Agricultural Data Scientist to strengthen supply chain optimization using big data from farm to market. You will develop predictive models for yields, demand, and inventory, integrate sensors, ERP, logistics, and market data, and present insights to stakeholders across the organization.

The role requires a strong background in statistics, machine learning, and agricultural value chains.

Qualifications

• Master's or Ph.D. in Data Science, Statistics, Computer Science, Agricultural Economics, or related field.
• Proficiency in programming languages such as Python or R and SQL for data extraction.

Responsibilities

• Develop advanced analytical models to forecast yields, demand, and inventory.
• Analyze supply chain performance metrics to identify bottlenecks and improvements.
• Utilize machine learning algorithms to predict price fluctuations and sourcing strategies.
• Integrate data from farm sensors, ERP systems, logistics providers, and market intelligence platforms.
• Create data visualizations and dashboards to communicate insights to stakeholders.
• Collaborate with operations, procurement, and sales teams to implement data-driven strategies.
• Evaluate and recommend new data sources, tools, and technologies to enhance analytics.
• Ensure data integrity, accuracy, and security throughout the process.

Skills

Python or R

SQL

Statistical modeling

Machine learning

Time series analysis

Big data

Cloud platforms

Communication

Education

Master's or PhD in a quantitative field

Tools

Spark

Hadoop

Job description

Placements24 in Durban, KwaZulu-Natal, is seeking an Agricultural Data Scientist to strengthen supply chain optimization using big data from farm to market. You will develop predictive models for yields, demand, and inventory, integrate sensors, ERP, logistics, and market data, and present insights to stakeholders across the organization.

The role requires a strong background in statistics, machine learning, and agricultural value chains.