Data Scientist - AI/ML
SCIENCE SYSTEMS & APPLICATIONS
Seabrook, United States of America
5 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Compensation
$ 105KJob location
Seabrook, United States of America
Tech stack
JavaScript
Geographic Information Systems
Artificial Intelligence
Amazon Web Services (AWS)
Data analysis
ArcGIS (Software)
Azure
Cloud Database
Computer Programming
Data as a Services
Data Visualization
Linux
Fortran
Spatial Databases
Geospatial Intelligence
Python
Machine Learning
Node.js
Quantum Computing
Cloud Services
Scientific Computating
Software Systems
SQL Databases
Web Applications
Web Services
Data Processing
Enterprise Software Applications
High Performance Computing
Geospatial Data Abstraction Library (GDAL)
Machine Learning Operations
Tools for Reporting
REST
Data Pipelines
Job description
- Design, develop, and deploy machine learning and artificial intelligence models to support predictive analytics, environmental monitoring, geospatial intelligence, and Earth science research applications.
- Develop geospatial analytics, visualization tools, and web-based applications for large environmental and remote sensing datasets.
- Create and support RESTful APIs, web services, and cloud-based data access systems utilizing AWS and Azure cloud platforms for scalable storage, processing, and dissemination of Earth science data.
- Develop workflows for processing, quality control, analysis, and dissemination of satellite-derived geospatial products.
- Collaborate with scientists to translate research requirements into operational software solutions.
- Support Earth science data archives, user services, and community engagement activities.
- Perform spatial and temporal analysis of environmental datasets using GIS, remote sensing, and statistical methods.
- Develop and automate data processing pipelines using Python and scientific computing frameworks, leveraging cloud services to support large-scale geospatial and remote sensing workflows.
- Support, maintain, and integrate legacy scientific applications developed in Fortran with modern Python-based analytics, data processing, and visualization workflows.
- Integrate diverse datasets from satellite observations, field measurements, and numerical models.
- Prepare technical documentation, scientific reports, conference presentations, and peer-reviewed publications.
- Collaborate with scientists, software engineers, and technology partners to identify opportunities for integrating quantum computing concepts into AI/ML workflows, high-performance computing environments, and next-generation Earth science applications.
Requirements
- Master's Degree (M.S.) and a minimum of 5 years related experience and/or training, or equivalent combination of education and experience.
- Experience applying AI/ML techniques to Earth science, climate, environmental, geospatial, remote sensing, or other large scientific datasets.
- Strong programming skills in Python and experience with scientific computing workflows, including Fortran and high-performance computing environments.
- Experience developing and deploying AI/ML solutions using cloud platforms such as AWS and Azure.
- Experience with GIS and geospatial data processing tools.
- Experience working with large environmental, remote sensing, or geospatial datasets.
- Knowledge of spatial databases, web services, application development frameworks, and emerging technologies such as quantum computing.
- Experience developing and supporting data visualization and analytics tools.
- Strong written and verbal communication skills.
- Ability to work effectively in multidisciplinary scientific teams., * Experience supporting NASA, NOAA, USGS, or other federal Earth science programs.
- Experience with satellite data products such as MODIS, Landsat, VIIRS, or similar Earth observation datasets.
- Experience developing geospatial web applications and cloud-enabled data services.
- Knowledge of GDAL, ArcGIS, GRASS GIS, or similar geospatial software packages.
- Experience with JavaScript, Node.js, SQL, Linux, and scientific computing environments.
- Experience with hydrologic, environmental, ecological, or climate modeling.
- Demonstrated record of peer-reviewed scientific publications.
- Experience interacting directly with scientific user communities and stakeholders.