Job Description

East Coast – Remote.

Position Overview

Position Overview

We are seeking a talented and driven Data Scientist to join our innovative team. As a leader in geospatial analytics and environmental monitoring, our organization leverages cutting-edge technology and advanced data science to unlock insights about the Earth’s ecosystems. In this role, you will work on complex datasets, develop predictive models, and create solutions that empower clients to make informed decisions in addressing environmental challenges.

Key Responsibilities

• Data Analysis and Modeling : Analyze large and complex geospatial datasets, including satellite imagery, remote sensing data, and environmental datasets, to extract actionable insights.
• Algorithm Development : Design and implement machine learning and statistical algorithms to solve problems related to land use, carbon sequestration, biodiversity, and other ecological metrics.
• Data Integration : Combine diverse data sources to create enriched datasets for analysis, including structured, unstructured, and spatial data.
• Visualization and Reporting : Develop clear and impactful visualizations to communicate findings to both technical and non-technical stakeholders.
• Collaboration : Work closely with software engineers, geospatial analysts, and domain experts to integrate data science solutions into our platform and products.
• Innovation : Stay current with the latest advancements in data science, machine learning, and geospatial technology, and apply these techniques to improve our capabilities.
• Data Quality and Ethics : Ensure high standards of data quality and promote ethical use of data in all analyses and models.

Qualifications

Required

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Environmental Science, or a related field.
• Proficiency in programming languages such as Python, R, or JavaScript, with experience in data science libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong skills in geospatial tools and libraries (e.g., QGIS, GDAL, GeoPandas, or ArcGIS).
• Solid understanding of statistical modeling, machine learning algorithms, and data preprocessing techniques.
• Experience with cloud computing platforms such as AWS, Google Cloud, or Azure.
• Proven ability to work with large datasets and databases (SQL and NoSQL).

Preferred

• Knowledge of remote sensing technologies and image processing.
• Experience with time-series analysis and predictive modeling in environmental contexts.
• Familiarity with big data tools (e.g., Spark, Hadoop).
• Strong communication skills with the ability to convey technical information to non-technical audiences.
• Passion for environmental conservation and sustainability.

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