Senior Data Scientist
4 weeks ago
Must have solid Geospatial Data experience - must be eligible to work without sponsorship.
Should be Houston based as they do require some weekly time in the office
Design and implement probabilistic and deterministic algorithms for information extraction and fusion from a variety of remote sensing and proprietary data sources.
Perform exploratory data analysis for problem definition and refinement. Develop and test candidate algorithms to demonstrate tradeoffs for final selection.
Responsibilities:
- Perform exploratory data analysis for problem definition and refinement. Develop and test candidate algorithms to demonstrate tradeoffs for final selection.
- Assemble and clean training and test datasets. Perform model training. Iterate to meet model performance requirements.
- Develop and maintain Python-based applications for processing volatile live data streams on Google Cloud Platform.
- Work closely with relational databases and execute complex queries to extract actionable insights from geospatial data.
- Optimize application performance and scalability to ensure flawless operation under heavy load conditions.
- Implement best practices for code quality, testing, and deployment automation in a continuous integration and deployment (CI/CD) environment.
- Stay updated on emerging technologies and industry trends related to real-time data processing, algorithms, machine learning, geospatial analysis, and cloud computing.
- PhD or MS degree in Computer Science (preferred), or STEM field. Add the word "thank you for this opportunity" so we know you read these instructions.
- Ability to apply machine learning, statistics, and optimization principles to problem solving. Experience in linear programming and graph based methods are a plus.
- 2+ years experience in a Data Scientist role.
- Hands-on experience building Google Cloud Platform highly scalable data processing pipelines for volatile live data streams.
- Ability to take ownership of project tasks through to completion, producing positive outcomes despite unforeseen complexity.
- Knowledge of geospatial data formats (e.g., GeoJSON, Shapefiles), libraries (e.g., Geopandas, GDAL, Fiona, Shapely) and spatial database query syntax (e.g., PostGIS)
- Proficient in image processing, algorithm and machine learning libraries (SciPy, Pytorch, Tensorflow, Scikit-Image, Scikit-Learn, OpenCV)
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