Agricultural Data Scientist & Farm Analytics Lead

Position Overview

Join a forward-thinking agricultural technology company as an Agricultural Data Scientist & Farm Analytics Lead, based in St. Louis, Missouri, US. This role offers a hybrid work arrangement, blending the collaborative environment of our office with the flexibility of remote work. You will be instrumental in leveraging vast datasets to optimize crop yields, improve resource management, and drive innovation across agricultural operations.

Key Responsibilities

• Develop and deploy sophisticated data models to analyze agricultural data including soil conditions, weather patterns, crop health, and market trends.
• Utilize machine learning algorithms and statistical techniques to predict crop yields, identify disease outbreaks, and optimize irrigation and fertilization strategies.
• Design and implement data visualization dashboards to present complex findings to stakeholders such as farmers, agronomists, and executive leadership.
• Collaborate with cross-functional teams including agronomists, field technicians, and software engineers to integrate data-driven insights into practical farming solutions.
• Oversee the collection, cleaning, and preprocessing of diverse agricultural datasets from various sources like sensors, drones, satellites, and manual inputs.
• Conduct A/B testing and performance analysis on new agricultural technologies and practices.
• Stay abreast of the latest advancements in agricultural technology, data science, and machine learning relevant to the sector.
• Mentor junior data analysts and contribute to the development of the company's data science capabilities.
• Ensure data integrity and security across all analytical processes.
• Present research findings and recommendations at industry conferences and internal meetings.

Required Qualifications

• Master's or Ph.D. in Data Science, Statistics, Computer Science, Agricultural Science, or a related quantitative field.
• 5+ years of experience in data science with a significant focus on agricultural applications or complex biological systems.
• Proven expertise in statistical modeling, machine learning, and predictive analytics.
• Proficiency in programming languages such as Python or R and experience with libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow).
• Experience with SQL and database management.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills with the ability to explain complex technical concepts to non-technical audiences.
• Experience with data visualization tools (e.g., Tableau, Power BI).
• Ability to work effectively in both independent and team-oriented settings within a hybrid work model.

Preferred Qualifications

• Familiarity with big data technologies (e.g., Hadoop, Spark) is a plus.
• Understanding of agricultural practices, crop science, and farm management is highly desirable.