Remote Agricultural Data Scientist - Predictive Analytics

Placements24; Centurion; Hybrid; ZAR 600,000 - 1,200,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Remote work flexibility

Job summary

Placements24 seeks a highly analytical Agricultural Data Scientist to work remotely on big data projects for the global agriculture sector. You will collect, clean, and analyze diverse datasets, develop predictive models for yields, disease risk, and efficiency, and communicate insights to varied stakeholders.

The role emphasizes collaboration with agronomists and engineers, ongoing learning in ML advances, and delivering data-driven strategies that improve productivity and sustainability from

Qualifications

• Requires a Master's or PhD in a quantitative field with strong agricultural knowledge.
• Minimum 5 years of data science experience with ML/statistical modeling in real-world projects.
• Proficiency in Python or R and relevant libraries; strong SQL/NoSQL and big data tooling.

Responsibilities

• Collect, clean, and analyze large agricultural datasets from multiple sources.
• Build, validate, and deploy predictive models for crop yields, diseases, pests, and resource use.
• Design experiments and A/B tests to evaluate interventions and optimize outcomes.
• Create data visualizations and reports for technical and non-technical stakeholders.
• Collaborate with agronomists, engineers, and product teams to implement insights.

Skills

Python

R

Big data analytics

Data storytelling

Remote collaboration

Education

Master's or PhD in Data Science/Statistics/CS/Agri Science

Tools

Pandas

NumPy

Scikit-learn

TensorFlow

PyTorch

SQL

NoSQL

Spark

Hadoop

Tableau/Power BI

Job description

Placements24 seeks a highly analytical Agricultural Data Scientist to work remotely on big data projects for the global agriculture sector. You will collect, clean, and analyze diverse datasets, develop predictive models for yields, disease risk, and efficiency, and communicate insights to varied stakeholders.

The role emphasizes collaboration with agronomists and engineers, ongoing learning in ML advances, and delivering data-driven strategies that improve productivity and sustainability from