Job description

Our client, a pioneering firm in the agricultural technology sector, is seeking an innovative Senior Agricultural Data Scientist to contribute to their mission of revolutionizing modern farming practices. Based in Columbus, Ohio, US , this role offers a hybrid work model, allowing for collaboration in the office and focused work remotely. You will leverage advanced analytical techniques, machine learning, and statistical modeling to extract actionable insights from vast agricultural datasets, including crop yields, weather patterns, soil conditions, and sensor data. Your work will directly impact operational efficiency, sustainability, and profitability for agricultural enterprises. Key Responsibilities: Develop and implement predictive models for crop yield forecasting, disease detection, and pest management. Analyze large datasets from IoT sensors, drones, satellite imagery, and farm management systems to identify trends and optimize resource allocation (e.g., water, fertilizer). Design and conduct statistical analyses to assess the impact of different farming techniques and environmental factors on crop performance. Build and maintain data pipelines for ingesting, cleaning, and processing diverse agricultural data sources. Collaborate with agronomists, researchers, and engineers to translate complex data findings into practical, actionable recommendations for farmers. Develop machine learning algorithms for optimizing irrigation schedules, nutrient application, and harvesting times. Visualize complex data and present findings clearly and concisely to technical and non-technical stakeholders. Stay current with advancements in agricultural science, data science, and relevant technologies. Contribute to the development of new data-driven products and services for the agricultural industry. Ensure data quality, integrity, and security throughout all analytical processes. Qualifications: Master's or Ph.D. in Data Science, Statistics, Agricultural Science, Computer Science, or a related quantitative field. 5+ years of experience in data science, with a strong focus on agricultural applications. Proficiency in programming languages such as Python or R, and expertise in relevant libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch). Experience with SQL and NoSQL databases. Strong understanding of statistical modeling, machine learning techniques, and experimental design. Familiarity with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, Azure, GCP). Knowledge of agricultural practices, crop science, or related domains is highly desirable. Excellent problem-solving skills and the ability to work with large, complex datasets. Strong communication and presentation skills, with the ability to explain technical concepts to diverse audiences. Ability to work effectively in a hybrid team environment.

Requirements