Smart Agriculture Data Scientist Job Vacancy in Mufulira, Zambia – Agriculture Technology

Job Description

Position:Smart Agriculture Data Scientist

Company:Bayer Zambia

Location:Mufulira, Zambia

Experience:3-5 years of relevant experience in data analytics, machine learning, or agronomy

Education:Bachelor’s degree in Data Science, Computer Science, Agriculture, or related field

Employment Type:Full-time

Industry:Agriculture Technology

Department:Research & Development

Salary:SSP 120,000 – 180,000 per month

Vacancies:1

Company Overview

Bayer Zambia is a leading global life sciences company dedicated to improving the health and sustainability of crops worldwide. With a strong presence in Zambia, Bayer combines cutting‑edge research, innovative technologies, and local expertise to support farmers in achieving higher yields, better resource efficiency, and resilient agricultural practices. As part of Bayer’s commitment to digital transformation in agriculture, the company is expanding its data‑driven capabilities to empower growers with actionable insights.

Our mission is to harness science and technology to create a more sustainable food system. By joining Bayer Zambia, you become part of a diverse team that values collaboration, integrity, and continuous learning. We are actively hiring in Zambia to strengthen our regional footprint and drive forward the future of smart farming.

For more information about our global initiatives, visit our main website: https://zambiajobssearch.com.

Job Overview

The Smart Agriculture Data Scientist will play a pivotal role in developing advanced analytics solutions that transform raw agricultural data into strategic recommendations for farmers, agronomists, and supply‑chain partners. You will work closely with cross‑functional teams, including agronomy, product development, and IT, to design predictive models, optimize field trials, and support the rollout of Bayer’s digital farming platforms across Zambia.

This role offers a unique opportunity to blend data science expertise with agricultural knowledge, contributing directly to the success of Zambia’s farming community and the broader goals of sustainable agriculture. Your work will influence decision‑making at the farm level, helping to increase productivity while reducing environmental impact.

Explore related opportunities in neighboring markets: https://mozambiquejobsearch.com/job-vacancy-mozambique/.

Key Responsibilities

• Collect, clean, and integrate heterogeneous data sources such as satellite imagery, sensor data, weather forecasts, and farm management records.
• Develop and validate machine‑learning models for yield prediction, disease detection, and input optimization.
• Collaborate with agronomists to translate model outputs into practical field recommendations.
• Design interactive dashboards and visualizations for stakeholders using tools like Power BI or Tableau.
• Conduct field trials to assess model performance and iterate on algorithms based on real‑world feedback.
• Document methodologies, code, and results following best practices for reproducibility.
• Stay abreast of emerging technologies in precision agriculture, AI, and remote sensing.

Required Skills

• Proficiency in Python or R, with experience in libraries such as scikit‑learn, TensorFlow, or PyTorch.
• Strong knowledge of SQL and data warehousing concepts.
• Familiarity with GIS tools (e.g., QGIS, ArcGIS) and remote sensing data.
• Understanding of agronomic principles and crop physiology is highly desirable.
• Excellent problem‑solving abilities and a data‑driven mindset.
• Effective communication skills to convey complex insights to non‑technical audiences.

Education

A minimum of a Bachelor’s degree in Data Science, Computer Science, Agricultural Engineering, Agronomy, or a related discipline is required. Advanced degrees (MSc or PhD) in a quantitative field are considered an asset.

Experience

Applicants should have 3‑5 years of professional experience in data analytics, machine learning, or related roles, preferably within the agriculture sector. Experience with precision farming platforms or agritech startups will be viewed favorably.

Salary

The compensation package ranges from SSP 120,000 to SSP 180,000 per month, commensurate with experience and qualifications. In addition to base salary, Bayer offers performance‑based bonuses and a comprehensive benefits suite.

Benefits

• Health insurance covering medical, dental, and vision care.
• Retirement savings plan with employer contributions.
• Paid annual leave and statutory holidays.
• Professional development allowance for certifications and conferences.
• Employee assistance program and wellness initiatives.

Training

• On‑boarding program covering Bayer’s agronomy solutions and data infrastructure.
• Access to internal learning platforms for AI, GIS, and agronomy courses.
• Mentorship from senior data scientists and agronomists.
• Opportunities to attend international Bayer research symposiums.

Working Environment

Bayer Zambia offers a collaborative, inclusive, and innovative workplace. Our offices in Mufulira are equipped with modern workstations, high‑speed internet, and dedicated spaces for brainstorming and data visualization. Field work may involve travel to farms across the Copperbelt Province, providing hands‑on exposure to real‑world agricultural challenges.

Application Process

Interested candidates should submit their updated CV and a cover letter outlining their relevant experience through our online portal. Applications will be reviewed on a rolling basis, and shortlisted candidates will be invited for a virtual interview followed by an on‑site technical assessment.

For additional career resources, visit: https://malawijobsearch.com/.

Equal Opportunity Statement

Bayer Zambia is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of gender, race, religion, age, disability, or sexual orientation. All qualified applicants will receive consideration for employment without regard to any protected characteristic.