Data Scientist - AI/ML

SCIENCE SYSTEMS & APPLICATIONS

Lanham, MD, United States

1 day ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.juju.com

Tech stack

JavaScript (Programming Language) Geographic Information Systems Artificial Intelligence Amazon Web Services Data Analysis ArcGIS (Software) Microsoft Azure Cloud Database Computer Programming Data as a Services Data Visualization Linux

+21 more

Fortran (Programming Language) Spatial Databases Geospatial Intelligence Python (Programming Language) 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 Restful APIs 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.