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Remote Sensing & Geospatial Data Intern

Remote · Internship · 3 – 6 Months

About the Internship

We are looking for a motivated Remote Sensing and Geospatial Data Intern to support a research-driven climate-risk project focused on South Asia.

The project uses publicly available climate and Earth-observation data to study crop stress, rainfall patterns, flood conditions, and drought risk. The aim is to build transparent and reproducible data workflows that can support climate resilience, agricultural monitoring, and environmental-risk research.

This role is suitable for students or early-career candidates interested in Python, data science, AI, remote sensing, GIS, agriculture, environmental analysis, climate adaptation, or disaster-risk reduction. Prior specialist experience in satellite imagery is welcome but not required; curiosity, strong data fundamentals, and willingness to learn are essential.

Key Responsibilities

• Clean, analyse, and visualise climate and environmental datasets using Python.
• Support the calculation and interpretation of vegetation, rainfall, drought, and flood-related indicators.
• Build clear, reproducible Jupyter or Google Colab notebooks for data analysis.
• Create charts, tables, summaries, and technical documentation for research outputs.
• Assist with quality checks, validation, and interpretation of data-driven indicators.
• Help organise datasets, experiment outputs, and code using good version-control and documentation practices.
• Participate in remote research meetings and provide concise progress updates.
• Contribute to a collaborative, evidence-based research workflow.

Required Qualifications

• Currently studying, or recently graduated from, Computer Science, Data Science, Geospatial Science, Remote Sensing, Environmental Science, Engineering, Agriculture Technology, or a related field.
• Working knowledge of Python.
• Familiarity with data-analysis libraries such as Pandas, NumPy, Matplotlib, Seaborn, or equivalent tools.
• Basic understanding of data cleaning, exploratory data analysis, statistics, and visualisation.
• Ability to write organised and reproducible notebooks or scripts.
• Comfortable working independently in a remote and asynchronous environment.
• Strong written communication skills and attention to detail.
• Willingness to learn unfamiliar technical concepts and ask thoughtful questions.

Preferred Skills (Not Required)

• Experience with Google Earth Engine, QGIS, ArcGIS, GeoPandas, Rasterio, Xarray, PostGIS, or SQL.
• Exposure to satellite imagery, weather data, climate data, GIS, hydrology, agriculture, or environmental research.
• Experience with Git, GitHub, APIs, Docker, cloud platforms, or data pipelines.
• Experience with machine learning, time-series analysis, remote sensing, or spatial-data visualisation.
• Interest in climate resilience, agricultural technology, flood risk, drought monitoring, or disaster-risk reduction.

What You Will Learn

• How to work with real climate and Earth-observation data.
• How to build reproducible Python workflows for environmental-data analysis.
• Practical skills in data validation, uncertainty-aware interpretation, and technical documentation.
• Foundations of remote sensing, including vegetation, rainfall, drought, and flood-risk indicators.
• Research practices for turning raw data into clear technical findings.
• Collaborative software-development practices, including version control and well-documented code.

What You Will Gain

• Hands-on experience with an applied climate-resilience and geospatial-data research project.
• Mentorship in Python data analysis, reproducible research, and remote-sensing fundamentals.
• Portfolio-ready notebooks, visualisations, and technical contributions where appropriate.
• Experience working with real-world environmental datasets and research questions.
• Potential acknowledgement or authorship opportunities where the level of contribution meets applicable research standards.
• A structured opportunity to build skills at the intersection of data science, climate, agriculture, and geospatial technology.

Selection Process

1. Review of CV/resume, technical background, and relevant project links.
2. A short online interview to discuss motivation, availability, Python/data skills, and learning approach.
3. A short Google Colab coding assessment using a small climate-data dataset.
4. Final discussion regarding start date, expected commitment, and internship goals.

The coding assessment focuses on Python data cleaning, time-series summaries, charts, reproducible analysis, and careful interpretation of uncertainty. It does not require prior advanced remote-sensing experience.

How to Apply

Please submit the following:

• Your CV or resume.
• A short note explaining your interest in this internship.
• Links to GitHub, portfolio, Google Colab notebooks, Kaggle, or relevant projects, if available.
• Your expected weekly availability and preferred start date.

Note: This is a research and technical learning opportunity. Project-specific methods, partner information, precise locations, internal thresholds, commercial plans, and other confidential details will be shared only with the selected candidate where necessary.

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