About Us:

We are a forward-thinking startup developing a satellite imagery analysis platform designed to transform agriculture, commodity trading, and insurance industries. Our platform uses real-time satellite data and machine learning to provide actionable insights into crop health, yield forecasts, and risk management.

We are looking for a talented Data Scientist / Machine Learning Engineer to develop, implement, and optimize the predictive models that power our platform. Your work will help drive data-driven decision-making in key industries, impacting global agriculture and financial markets.

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Responsibilities:

As a Data Scientist / Machine Learning Engineer, you will play a central role in building machine learning models that process and analyze satellite imagery and associated geospatial data. Your key responsibilities will include:

• Develop and deploy machine learning models for crop health analysis, yield prediction, and risk assessment using satellite imagery and geospatial datasets.
• Work with multispectral satellite data (e.g., from Sentinel-2, Landsat, or commercial satellites) to extract relevant features (e.g., vegetation indices, soil moisture, weather conditions).
• Design and implement scalable data pipelines to process large volumes of satellite imagery, combining data from multiple sources such as weather data, soil data, and historical crop yield data.
• Optimize machine learning algorithms for performance and accuracy, ensuring models can operate efficiently on large datasets with minimal latency.
• Collaborate with backend engineers and GIS specialists to integrate models into our platform, providing real-time and historical insights to users.
• Conduct exploratory data analysis (EDA) to discover trends and patterns in satellite and agriculture data.
• Stay up-to-date with the latest advancements in machine learning, computer vision, and remote sensing, applying cutting-edge techniques to enhance our platform.
• Perform model evaluation and tuning to improve predictive performance, including cross-validation, hyperparameter tuning, and error analysis.
• Communicate results to the team and non-technical stakeholders, providing insights into the real-world applications of your models.

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Requirements:

• 3+ years of experience in data science or machine learning, with a focus on working with large datasets and building predictive models.
• Expertise in Python and key data science libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow or PyTorch.
• Strong understanding of machine learning algorithms (e.g., regression, classification, time-series forecasting, deep learning) and experience applying them to real-world problems.
• Experience working with satellite imagery and geospatial data, including familiarity with libraries such as GDAL, Rasterio, and Geopandas.
• Proficiency in working with multispectral data (e.g., NDVI, EVI) for crop monitoring and vegetation analysis.
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and distributed computing frameworks (e.g., Apache Spark, Dask) for handling large datasets.
• Strong mathematical and statistical skills, with an ability to apply these in the context of data modeling and interpretation.
• Knowledge of geospatial tools and platforms such as QGIS, PostGIS, or similar.
• Ability to work in a collaborative, fast-paced startup environment and communicate technical concepts to non-technical team members.
• Bonus:
  • Experience with computer vision techniques (e.g., CNNs, object detection) applied to satellite imagery.
  • Familiarity with time-series forecasting models for yield prediction or climate-related risks.

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Benefits:

• Competitive salary with the flexibility of remote work.
• Work on a mission-driven project that leverages AI and satellite data to address key challenges in agriculture, commodities, and insurance.
• Be part of a dynamic, global team of experts in software engineering, data science, and GIS.
• Opportunities for professional growth within a rapidly scaling company.
• Access to state-of-the-art technologies and satellite data for real-world applications.

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How to Apply:

To apply, please submit your resume, portfolio, and a cover letter describing your experience with machine learning and satellite imagery. Additionally, please share relevant projects or case studies demonstrating your ability to apply data science and machine learning to real-world problems.

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We are committed to building a diverse and inclusive team and welcome applicants from all backgrounds. Join us in revolutionizing the use of satellite imagery and geospatial analysis in agriculture and financial markets!