Job Description & How to Apply Below
Role Overview
We are hiring two Data Scientists to advance Super GeoAI’s AI-powered agricultural solutions. In this role, you will lead the collection, labeling, and development of AI training datasets, turning complex geospatial, drone, and agricultural data into insights that fuel next-generation products. You will design and test models, conduct deep analysis, and work closely with AI engineers, software developers, and product managers to ensure our solutions deliver measurable value to farmers, insurers, and the global agriculture sector.
Key Responsibilities
Collect, curate, and label datasets to build high-quality AI training databases.
Analyze and preprocess large, diverse datasets (geospatial, drone imagery, climate, and agricultural data).
Develop and validate machine learning and statistical models to solve real-world problems in agriculture and insurance.
Collaborate with AI engineers to refine and product ionize algorithms for scalability and performance.
Design experiments and evaluations to measure model performance, reliability, and field impact.
Visualize and communicate findings to technical and non-technical stakeholders in clear, actionable ways.
Translate business requirements into data-driven solutions in collaboration with product managers.
Stay up to date with advances in data science, AI, and geospatial analytics to keep SGA’s technology at the cutting edge.
Qualifications
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field (Ph.D. preferred).
Proven experience applying machine learning and statistical techniques to large, complex datasets.
Strong proficiency in Python and core data science libraries (Num Py, Pandas, Scikit-learn).
Experience with deep learning frameworks (Tensor Flow, PyTorch) is a strong asset.
Familiarity with geospatial data and GIS tools (e.g., GDAL, QGIS, ArcGIS) is preferred.
Proficiency in data visualization (Matplotlib, Seaborn, Plotly, Tableau, or similar).
Strong understanding of data pipelines, feature engineering, and model evaluation metrics.
Excellent problem-solving, analytical, and communication skills.
Ability to work independently and collaboratively in a fast-paced startup environment.
This isn’t just data analysis—you’ll be transforming raw data into AI-powered insights that redefine the future of agriculture.
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