Agricultural Data Scientist

Placements24; Queenstown; Hybrid; NZD 110,000 - 160,000; Full time

Benefits offered by this job

Competitive salary

Health insurance

Retirement fund

Professional development

Hybrid work

Generous annual leave

Job summary

Placements24 is seeking an Agricultural Data Scientist to unlock insights from large agricultural datasets and improve decision-making across crop yields, soil health, and farm operations.

You will develop predictive models, collaborate with agronomists and software developers, and build dashboards to support farm management. A strong background in data science, ML, and agriculture is required, with hybrid work flexibility and opportunities for impact.

Qualifications

• Master's degree or PhD in Data Science, Statistics, Computer Science, Agricultural Science, or a related quantitative field.
• Minimum of 4 years of experience as a Data Scientist with proven analytics experience.
• Proficiency in Python or R and data science libraries (Pandas, NumPy, Scikit-learn).
• Experience with database management (SQL, NoSQL) and data warehousing concepts.
• Solid understanding of ML algorithms, statistical modeling, and experimental design.
• Familiarity with agricultural concepts and farm operations is advantageous.
• Excellent data visualization skills (Matplotlib, Seaborn, Tableau).
• Strong analytical, problem-solving, and communication abilities.
• Ability to work in a hybrid environment and collaborate with diverse stakeholders.

Responsibilities

• Collect, process, and manage large agricultural datasets from sensors, equipment, and weather stations.
• Develop and implement statistical models and ML algorithms for predictive analysis.
• Analyze data to identify trends and correlations in crop performance and soil health.
• Visualize data and communicate findings to technical and non-technical audiences.
• Collaborate with agronomists and farm managers to translate insights into actions.
• Develop data-driven tools and dashboards to support farm decisions.
• Evaluate interventions and strategies based on data analysis.
• Stay up-to-date with advances in data science and ag tech.
• Ensure data quality, integrity, and security across datasets.

Skills

Data analysis

Machine learning

Statistical modeling

Data visualization

Python / R

Education

Master's or PhD in Data Science / related field

Tools

Python

R

Pandas

NumPy

Scikit-learn

SQL

NoSQL

Job description

About the Role

Our client, a forward-thinking agricultural technology company with operations near Queenstown, Eastern Cape, is looking for a talented Agricultural Data Scientist. This role is crucial for unlocking insights from vast amounts of agricultural data to improve decision-making, enhance farm productivity, and promote sustainable practices. You will be responsible for collecting, cleaning, analyzing, and interpreting complex datasets related to crop yields, soil conditions, weather patterns, and farm operations. The ideal candidate will possess a strong background in data science, statistical modeling, machine learning, and a solid understanding of agricultural principles. You will work with various stakeholders, including agronomists, farm managers, and software developers, to develop data-driven solutions. This position offers the opportunity to leverage cutting-edge technologies to solve real-world agricultural challenges. You will play a key role in developing predictive models and actionable insights that can drive significant improvements in farming efficiency and output. The Queenstown area provides a unique setting to apply data science to diverse agricultural landscapes. This is a challenging and rewarding opportunity for a data enthusiast passionate about the future of agriculture. You will be at the forefront of integrating data into every aspect of modern farming.

Key Responsibilities

• Collect, process, and manage large agricultural datasets from various sources (sensors, farm equipment, weather stations, etc).
• Develop and implement statistical models and machine learning algorithms for predictive analysis (e.g., yield prediction, disease outbreak forecasting).
• Analyze data to identify trends, patterns, and correlations relevant to crop performance, soil health, and resource management.
• Visualize data and communicate findings clearly and effectively to technical and non-technical audiences.
• Collaborate with agronomists and farm managers to translate data insights into actionable recommendations.
• Develop data-driven tools and dashboards to support farm management decisions.
• Evaluate the effectiveness of agricultural interventions and strategies based on data analysis.
• Stay up-to-date with the latest advancements in data science, machine learning, and agricultural technology.
• Ensure data quality, integrity, and security across all datasets.
• Contribute to the development of data infrastructure and analytical frameworks.

Requirements

• A Master's degree or PhD in Data Science, Statistics, Computer Science, Agricultural Science, or a related quantitative field.
• A minimum of 4 years of experience as a Data Scientist, with a proven track record in applying analytical techniques to complex datasets.
• Strong proficiency in programming languages such as Python or R, and relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn).
• Experience with database management (SQL, NoSQL) and data warehousing concepts.
• Solid understanding of machine learning algorithms, statistical modeling, and experimental design.
• Familiarity with agricultural concepts, crop science, and farm operations is highly advantageous.
• Excellent data visualization skills (e.g., Matplotlib, Seaborn, Tableau).
• Strong analytical and problem-solving abilities.
• Effective communication and collaboration skills, with the ability to explain technical concepts to diverse audiences.
• Ability to work independently and manage multiple projects in a hybrid work environment.

Benefits

• Competitive salary and potential for project-based bonuses.
• Comprehensive health insurance and retirement fund benefits.
• Opportunities for professional development, including training in advanced analytics and agricultural technologies.
• Exposure to innovative agricultural technology and research.
• A collaborative and intellectually stimulating work environment.
• The opportunity to make a tangible impact on agricultural sustainability and efficiency.
• Flexible working arrangements in a hybrid model.
• Generous annual leave.