Earth & Environmental Science Expert – AI Projects

Gramian Consulting

. Translate authentic Earth-science workflows into self-contained terminal-based benchmark tasks.

Posted 9/25/2026contractRemote • Turkey, Brazil, ColombiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in Earth Sciences with a focus on scientific programming and data processing. Proficient in building reproducible computational workflows and conducting geospatial analysis, numerical modeling, and environmental risk assessments.

Highest-signal resume keywords

Ph.D. In Earth SciencesExpertise In Climate ScienceStrong Programming Skills In PythonScientific Data Processing ExperienceLinux Environment Proficiency

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonRBashJuliaGeospatial AnalysisNumerical ModelingTime-Series AnalysisData ValidationScientific Quality ControlEnvironmental Risk Analysis

Tools & Technologies

Command-Line ToolsScientific LibrariesTerminal-Based Environments

Industry Keywords

Earth-System ScienceRemote SensingHydrologyGeophysicsAtmospheric Science

Tech Stack

Tools & technologies

LinuxPythonRemote Sensing

About the role

Key responsibilities & impact

Translate authentic Earth-science workflows into self-contained terminal-based benchmark tasks.
Prepare and structure geospatial, climate, atmospheric, geological, hydrological, or oceanographic datasets.
Build reproducible computational environments using scientific libraries and command-line tools.
Develop expert reference solutions using Python, R, Bash, Julia, or domain-specific software.
Design tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, and environmental risk analysis.
Define objective grading criteria for scientific outputs, data transformations, and spatial or temporal accuracy.
Validate coordinate systems, units, timestamps, missing-data handling, and scientific assumptions.
Create automated tests for numerical tolerances, file formats, metadata, and reproducibility.
Debug issues involving projections, large datasets, dependencies, performance, and numerical stability.
Document data provenance, expected outputs, edge cases, assumptions, and limitations.

Requirements

What you’ll need

Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences or a closely related field.
Deep expertise in at least one area such as climate science, atmospheric science, geophysics, oceanography, geology, hydrology, remote sensing, environmental modeling, or Earth-system science.
Strong programming skills in Python, R, Julia, Bash, or another scientific programming language.
Hands-on experience with scientific data processing, numerical modeling, geospatial analysis, environmental datasets, or time-series analysis.
Experience working in Linux or terminal-based environments.
Ability to build, debug, and validate reproducible scientific computational workflows.
Strong understanding of scientific quality control, spatial and temporal data, uncertainty, and numerical accuracy.