Remote Agro-Data Analyst - Crop Yield Forecasting

Placements24; Randburg; Hybrid; ZAR 900,000 - 1,500,000; Full time

Benefits offered by this job

Competitive salary

Fully remote work

Health and wellness benefits

Professional development

Job summary

Placements24 is seeking a Remote Agro-Data Analyst to join a globally distributed, fully remote team. You will develop and refine predictive models for crop yield forecasting using datasets from weather, soil, satellite imagery, and historical agricultural data.

The role emphasizes data mining, statistical analysis, and machine learning, with collaboration across agronomists, data scientists, and software engineers.

Qualifications

• Master's or PhD in Data Science, Statistics, Agricultural Economics, Computer Science, or related quantitative field.
• A minimum of 5 years of data analysis experience focused on agricultural data and/or predictive modeling.
• Strong proficiency in Python or R and data analysis libraries (Pandas, NumPy, Scikit-learn, TensorFlow, Keras).
• Experience with statistical modeling techniques, time-series analysis, and machine learning algorithms.
• Familiarity with geospatial data analysis tools and remote sensing principles is highly desirable.
• Proven ability to work with large, complex datasets.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to convey complex information clearly.
• Demonstrated ability to work independently and manage projects effectively in a remote setting.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) is a plus.

Responsibilities

• Collect, clean, and preprocess large datasets related to crop production, weather, soil conditions, and satellite imagery.
• Develop, validate, and deploy statistical and machine learning models for accurate crop yield prediction and forecasting.
• Analyze trends and patterns in agricultural data to identify key factors influencing crop performance.
• Utilize remote sensing data (e.g., NDVI, EVI) and geospatial analysis techniques to enhance yield models.
• Collaborate with agronomists, data scientists, and software engineers to integrate analytical findings into actionable insights.
• Monitor model performance and implement improvements or re-training as necessary.
• Generate reports, visualizations, and dashboards to communicate findings and model outputs to stakeholders.
• Stay current with the latest advancements in data science, machine learning, and agricultural analytics.
• Contribute to the development of new analytical tools and methodologies for agricultural data interpretation.

Skills

Data analysis

Statistical thinking

Problem solving

Communication skills

Project management

Education

Master's or PhD in Data Science / related quantitative field

Tools

Python

R

Pandas

NumPy

Scikit-learn

TensorFlow

Keras

Geospatial analysis tools

Job description

About the Role

Our client, a leading innovator in agricultural analytics and technology, is seeking a talented Remote Agro-Data Analyst to join their globally distributed, fully remote team. This role is pivotal in developing and refining predictive models for crop yield forecasting, leveraging vast datasets from remote sensing, weather patterns, and historical agricultural information. The successful candidate will be adept at data mining, statistical analysis, and machine learning techniques applied to agricultural challenges. This is an exceptional opportunity for a data-driven professional to contribute to global food security and agricultural efficiency from a remote work environment.

Key Responsibilities

• Collect, clean, and preprocess large datasets related to crop production, weather, soil conditions, and satellite imagery.
• Develop, validate, and deploy statistical and machine learning models for accurate crop yield prediction and forecasting.
• Analyze trends and patterns in agricultural data to identify key factors influencing crop performance.
• Utilize remote sensing data (e.g., NDVI, EVI) and geospatial analysis techniques to enhance yield models.
• Collaborate with agronomists, data scientists, and software engineers to integrate analytical findings into actionable insights.
• Monitor model performance and implement improvements or re-training as necessary.
• Generate reports, visualizations, and dashboards to communicate findings and model outputs to stakeholders.
• Stay current with the latest advancements in data science, machine learning, and agricultural analytics.
• Contribute to the development of new analytical tools and methodologies for agricultural data interpretation.

Requirements

• Master's or PhD degree in Data Science, Statistics, Agricultural Economics, Computer Science, or a related quantitative field.
• A minimum of 5 years of experience in data analysis, with a significant focus on agricultural data and/or predictive modeling.
• Strong proficiency in programming languages such as Python or R, and relevant data analysis libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, Keras).
• Experience with statistical modeling techniques, time-series analysis, and machine learning algorithms.
• Familiarity with geospatial data analysis tools and remote sensing principles is highly desirable.
• Proven ability to work with large, complex datasets.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to convey complex information clearly.
• Demonstrated ability to work independently and manage projects effectively in a remote setting.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) is a plus.

Benefits

• Our client offers a highly competitive salary, comprehensive health and wellness benefits, and a generous performance bonus.
• This fully remote position provides exceptional work-life balance and the opportunity to collaborate with a global team.
• Professional development and continuous learning are strongly encouraged and supported.
• Join a company at the forefront of agricultural technology and data-driven decision-making.