Intern — Junior Carbon & GHG Modeler

Varaha; India; On-site; INR 900,000 - 1,800,000; Full time

Benefits offered by this job

Biogeochemical models exposure

Bayesian statistics familiarity

Remote sensing data familiarity

AI/LLM-assisted workflows

Job summary

Varaha in India seeks a researcher to model soil carbon and greenhouse gases using process-based models like DayCent, DNDC, and DSSAT. You will calibrate, validate, and estimate sequestration across agricultural landscapes, contributing to scalable analyses.

Collaborate with teams to document methodologies, implement production-grade Python pipelines with geospatial workflows, and prepare technical notes for auditors and carbon market evaluators.

Qualifications

• Bachelor's or Master's degree in CS, applied math, computational biology, or related fields.
• Experience with production-grade Python and geospatial data handling.
• Experience with Git version control and statistical modeling.
• Familiarity with geospatial libraries such as GeoPandas or Rasterio.

Responsibilities

• Model Soil Carbon & GHGs: Set up, calibrate, and validate process-based models to estimate soil carbon sequestration and emission reductions across landscapes.
• Master Carbon Standards: Gain hands-on experience with registries and methodologies (e.g., Verra VM0042, VMD0053), covering validation, uncertainty quantification, and MVR development.
• Build Scalable Pipelines: Write reproducible, production-grade Python code and geospatial workflows to scale models across regions.
• Collaborate & Defend Scientific Models: Prepare technical documentation, data visualizations, and rebuttals for auditors and carbon market evaluators.

Skills

Strong logical and analytical thinking

Effective technical communication

Python programming

Git version control

Statistical modeling

Geospatial data handling

Education

Bachelor's or Master's degree in Computer Science (B.Tech / M.Tech preferred), Applied Mathematics, Computational Biology, or related fields

Tools

GeoPandas

Rasterio

GIS tools

Job description

• Model Soil Carbon & GHGs: Learn to set up, calibrate, and validate process-based models such as DayCent, DNDC, or DSSAT to estimate soil carbon sequestration and emission reductions across agricultural landscapes.
• Master Carbon Standards: Gain hands-on experience with leading carbon registries and methodologies (e.g., Verra VM0042 and VMD0053), covering structural validation, uncertainty quantification, and Model Validation Report (MVR) development.
• Build Scalable Pipelines: Write reproducible, production-grade Python code and geospatial workflows to scale our models across regions.
• Collaborate & Defend Scientific Models: Help prepare rigorous technical documentation, data visualizations, and scientific rebuttals for third-party auditors (VVBs) and carbon market evaluators.

What You Will Do & Learn

• Model Soil Carbon & GHGs: Learn to set up, calibrate, and validate process-based models such as DayCent, DNDC, or DSSAT to estimate soil carbon sequestration and emission reductions across agricultural landscapes.
• Master Carbon Standards: Gain hands-on experience with leading carbon registries and methodologies (e.g., Verra VM0042 and VMD0053), covering structural validation, uncertainty quantification, and Model Validation Report (MVR) development.
• Build Scalable Pipelines: Write reproducible, production-grade Python code and geospatial workflows to scale our models across regions.
• Collaborate & Defend Scientific Models: Help prepare rigorous technical documentation, data visualizations, and scientific rebuttals for third-party auditors (VVBs) and carbon market evaluators.

What We Are Looking For

Educational Background

• Bachelor's or Master's degree in Computer Science (B.Tech / M.Tech preferred), Applied Mathematics, Computational Biology, or related fields.

Core Skills

• Strong logical and analytical thinking, and effective technical communication.

Core Foundations

• Production-grade Python, Git version control, statistical modeling, and geospatial data handling.

Coding Toolkit

• Hands-on experience with Python libraries and geospatial tools (e.g., GeoPandas, Rasterio) or GIS tools.

BONUS POINTS (NICE-TO-HAVES)

• Prior exposure to biogeochemical models (DayCent, DNDC, DSSAT) through coursework, research, or internships.
• Familiarity with Bayesian statistics, machine learning surrogates, or remote sensing data (Sentinel-2, MODIS).
• Interest in agricultural systems across India, Southeast Asia, or Sub-Saharan Africa.
• Habit of using modern AI/LLM-assisted workflows to accelerate research, data processing, and code development.