Data Scientist - TSSCI w/Poly
Listed on 2026-01-09
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IT/Tech
Data Scientist, Machine Learning/ ML Engineer, AI Engineer, Data Analyst
Job Description
We are seeking a Geospatial Data Scientist with strong machine learning and data engineering skills to transform massive geospatial datasets into actionable insights. You will design and implement advanced algorithms for image analysis, spatial modeling, and predictive analytics to support mission critical decision making. This role combines deep technical expertise with creativity in applying AI/ML to geospatial intelligence challenges.
Equal Opportunity StatementWe are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.
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- Analyze large scale geospatial datasets and imagery to extract patterns, trends, and quantifiable insights.
- Write efficient, production ready code in Python, JavaScript, and auxiliary languages for data pipelines and visualization.
- Apply advanced statistical and machine learning techniques (e.g., PCA, clustering, regression, NLP) to spatial and non spatial data.
- Build predictive models and decision support tools for operational and strategic use cases.
- Develop and optimize deep learning pipelines for image classification, object detection, and feature extraction using CNNs, RNNs, and Transformers.
- Implement cutting edge workflows for automated data collection, preprocessing, and analysis.
- Leverage frameworks such as PyTorch, Tensor Flow, and Scikit learn for scalable ML solutions.
- Utilize geospatial libraries (GDAL, Shapely, Geo Pandas, Rasterio) to process raster and vector data.
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