Geospatial Data Analysis: Process and analyse diverse geospatial datasets related to forestry, such as satellite imagery, LiDAR data, GIS layers, topographic maps, and field survey data.
    Data Analysis and Interpretation: Apply statistical analysis techniques to explore and interpret raster, vector datasets. Identify trends, patterns, and correlations in data to uncover insights and opportunities. Perform advanced analytics, such as predictive modelling and segmentation, to support business goals.
    Data Cleaning and Processing: Identify relevant forestry datasets, perform data cleaning, and preprocess the tabular data to ensure high-quality input for analysis.
    Spatial Data Visualization: Create visually appealing and informative maps, graphs, and interactive visualisations to communicate geospatial insights effectively.
    Forest Monitoring and Management: Develop models and algorithms to monitor changes in forest cover, assess tree health, track deforestation, and detect potential environmental risks.
    Predictive Modeling: Apply machine learning techniques and statistical methods to build predictive models for forest growth, species distribution, and other forestry-related parameters.
    Deep learning: Develop deep learning models to detect the trees from satellite/drone images, quantify the carbon.
    Spatial Analysis Techniques: Utilize GIS tools and techniques to perform spatial analysis, proximity analysis, spatial interpolation, and spatial pattern recognition.
    Collaboration: Collaborate with forestry experts, environmental scientists, and other stakeholders to understand project requirements and incorporate domain knowledge into data analysis.
    Data-Driven Insights: Extract meaningful insights from geospatial data to inform decision-making processes for sustainable forest management and conservation efforts.
    Research and Innovation: Stay up-to-date with the latest advancements in geospatial technology, data science methodologies, and forestry research to continuously improve project outcomes.
    Documentation: Maintain detailed documentation of data sources, methodologies, and results for future reference and knowledge sharing.

• Strong programming skills in Python, R, or other relevant languages commonly used in geospatial data analysis.
• Expertise in machine learning and statistical modelling, with a focus on geospatial applications.
• Familiarity with forestry concepts, ecosystem dynamics, and environmental monitoring.
• Experience with geospatial data libraries and frameworks (e.g., GDAL, GeoPandas, Fiona, Rasterio) is a plus.
• Strong problem-solving skills and the ability to translate complex data into actionable insights.
• Excellent communication skills to present findings and recommendations to both technical and non-technical audiences.

Working at Earthtree:

•     This role is full-time with flexible hours.
•     Join a fast growing business with a great opportunity to quickly grow your career.
•     Join the offices in Shillong, Meghalaya, India or work fully remote and help us positively impact climate, biodiversity and communities in India and globally.
•     We do not discriminate on the basis of race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability. We ensure every applicant gets an equal chance of success.

Other benefits:

•     Learning and development: We are on a constant learning journey and you will receive an annual learning and development budget to help you with this.
•     Attractive leave policy,
•     Hybrid work policy.