Scientific Computing / Research Engineering Experts – Earth Sciences

Hello,

Greetings from ZettaMine!!!

We are hiring Scientific Computing / Research Engineering Experts – Earth Sciences for a short-term AI Research & Scientific Computing project across: 🇮🇳 India

Role: Scientific Computing / Research Engineering Expert – Earth Sciences

Experience: Ph.D., Postdoctoral Experience, or Equivalent Advanced Technical Experience

Mode: Remote

Engagement: Contractor / Short-Term Contract

Start: Immediate

Mandatory Eligibility Criteria

Technical Qualification:

• Strong programming skills in Python, R, Julia, Bash , or another relevant scientific programming language.
• Experience working in Linux or terminal-based environments .
• Strong expertise in at least one Earth Sciences domain, including Climate Science, Atmospheric Science, Geophysics, Oceanography, Geology, Hydrology, Environmental Modeling, Remote Sensing, Earth-System Science, or Geospatial & Environmental Data Science .
• Strong knowledge of numerical methods, scientific modeling, geospatial analysis, environmental data processing, time-series analysis, or quantitative data analysis.
• Ability to independently implement, test, debug, and validate computational Earth-science workflows.
• Strong understanding of coordinate systems, units, timestamps, missing-data handling, numerical precision, uncertainty, boundary conditions, spatial/temporal accuracy, and scientific reproducibility .
• Experience working with geospatial, climate, atmospheric, geological, hydrological, oceanographic, satellite, seismic, or environmental datasets .
• Ability to develop and validate computational workflows involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, or environmental risk analysis .
• Ability to debug issues involving geospatial projections, large datasets, dependencies, numerical stability, performance, scientific libraries, and file formats .
• Ability to develop rigorous scientific tasks with clearly defined inputs, expected outputs, numerical tolerances, and evaluation criteria.
• Ability to validate scientific outputs for numerical accuracy, physical consistency, spatial/temporal accuracy, and reproducibility.

Educational Qualification:

Mandatory: Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences, Environmental Science, Geophysics, Atmospheric Science, Oceanography, Geology, Hydrology, Climate Science, Remote Sensing , or another closely related scientific discipline.

Preferred: Advanced research or professional experience in Climate Science, Atmospheric Science, Geophysics, Oceanography, Geology, Hydrology, Environmental Modeling, Remote Sensing, Earth-System Science, Geospatial Science, Scientific Computing , or related fields.

Availability:

• Full-Time – 40 Hours per Week .
• Minimum 4 hours of PST overlap per day .
• Ability to work remotely on a short-term scientific computing project.
• Immediate availability preferred.
• Ability to collaborate with project reviewers and incorporate feedback.
• Availability according to project requirements and deadlines.

Others:

• Personal laptop/desktop and stable high-speed internet.
• Strong written and verbal communication skills.
• Experience working with scientific computing and terminal-based environments.
• Experience with scientific/geospatial tools such as NumPy, pandas, SciPy, xarray, rasterio, GeoPandas, Cartopy, GDAL , or similar technologies.
• Experience with scientific data formats such as NetCDF, HDF5, GeoTIFF, Shapefiles, or GRIB .
• Familiarity with Docker, Conda, Git, CI/CD, automated testing, or HPC environments .
• Ability to ensure computational tasks are reproducible and execute successfully without runtime downloads.
• Strong attention to scientific accuracy, documentation, data provenance, and reproducibility.
• Research software engineering, scientific benchmarking, or automated grader development experience is an advantage.
• Experience evaluating AI coding/terminal agents or developing tasks and evaluations for AI systems is preferred.
• Publications or open-source contributions in Earth, environmental, geospatial, or computational sciences are advantageous.
• Ability to provide an updated Google Scholar profile/link where applicable.

What You’ll Work On:

• Design authentic, multi-step Earth Sciences tasks based on realistic research and computational workflows.
• Translate authentic Earth-science workflows into self-contained terminal benchmark environments.
• Prepare geospatial, climate, atmospheric, geological, hydrological, oceanographic, satellite, seismic, or environmental datasets .
• Build reproducible computational environments using appropriate scientific libraries and command-line tools.
• Implement expert solutions using Python, R, Bash, Julia , or other relevant scientific/domain-specific tools.
• Create tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, environmental modeling, and environmental risk analysis .
• Develop automated tests for numerical accuracy, spatial/temporal accuracy, scientific consistency, numerical tolerances, metadata, file formats, and reproducibility .
• Define appropriate coordinate systems, units, timestamps, numerical tolerances, boundary conditions, missing-data handling, and expected scientific behavior .
• Validate that tasks are reproducible and execute successfully without runtime downloads.
• Debug issues involving geospatial projections, large datasets, dependencies, numerical precision, solver stability, performance, and file formats .
• Document input data provenance, scientific assumptions, computational requirements, expected outputs, edge cases, and known limitations .
• Develop benchmark tasks that evaluate whether AI agents can inspect scientific datasets, process geospatial and time-series data, reason through Earth-science problems, implement reliable computational models, operate command-line tools, troubleshoot scientific pipelines, and produce accurate, reproducible, and objectively verifiable scientific outputs.

Interested candidates kindly share your updated CV and Google Scholar profile/link to [email protected]

Thanks & Regards,

Praneeth.N

ZettaMine