Geospatial Data Scientist

Growth For Impact; Bengaluru; Hybrid; INR 600,000 - 900,000; Full time

Benefits offered by this job

Health insurance

Unlimited leaves

Remote work option

Relocation assistance

Mental Wellness services

Employee stock options

Job summary

CleanMax is seeking a geospatial ML engineer to build and evaluate ML models for crop and land-cover tasks in Bengaluru. You will prep hyperspectral and SAR datasets, fine-tune models, and contribute to our semantic search capabilities.

The role involves collaboration with data engineers, scientists, and product managers to move models from notebooks to production. Applicants should have 0–3 years of ML experience in geospatial or remote sensing, strong Python skills, and familiarity with

Qualifications

• 0–3 years of experience applying ML to geospatial data.
• Solid Python programming skills with ML libraries.
• Foundational spectral data understanding and hyperspectral interest.
• Familiarity with raster/vector formats and CRS concepts.
• Willingness to learn domain-specific methods on the job.

Responsibilities

• Build, train, and evaluate geospatial ML models as directed by senior team members.
• Prepare and preprocess hyperspectral, multispectral, and SAR data.
• Fine-tune and benchmark models on new tasks and datasets.
• Contribute to embedding- and segmentation-based search methods.
• Integrate multimodal data into modeling pipelines.
• Support transfer learning and domain adaptation efforts.
• Validate models against ground truth and known relations.
• Write clean, well-documented Python code and contribute to pipelines.
• Collaborate with data engineers, scientists, and product managers.
• Document methodology and communicate results to stakeholders.

Skills

Python

ML libraries (PyTorch)

ML libraries (TensorFlow)

ML libraries (scikit-learn)

Geospatial ML

Geospatial data handling

Spatial analysis

Communication

Tools

Rasterio

xarray

GDAL

PyProj

Job description

Responsibilities

• Build, train, and evaluate geospatial AI/ML models for applications such as crop classification, forest/biomass estimation, water quality retrieval, invasive species and land cover mapping, and change detection, under the direction of senior team members.
• Prepare and preprocess hyperspectral, multispectral, and SAR datasets: co-registration, atmospheric/radiometric correction, glint and shadow masking, chip generation, and label wrangling.
• Fine-tune and benchmark existing geospatial and foundation models on new tasks and datasets.
• Contribute to embedding- and segmentation-based approaches for large-scale image search and object-centric retrieval, as part of our broader platform’s semantic search capabilities.
• Integrate multimodal data sources: hyperspectral, SAR, weather, and ground-truth/in-situ data, into modeling pipelines.
• Support transfer learning and domain adaptation efforts to extend models from well-labeled to label-scarce regions.
• Validate models rigorously against ground truth and known physical/spectral relationships, and help build out shared validation tooling and benchmarks used across the team.
• Write clean, well-documented, and reasonably efficient Python code, and contribute to shared libraries and pipelines used by the broader analytics team.
• Collaborate with data engineers, solutions scientists, and product managers to move models from notebook to production pipeline.
• Document methodology, maintain experiment tracking, and clearly communicate results to both technical and non-technical stakeholders.

Requirements

• 0–3 years of experience applying machine learning to geospatial, remote sensing, or other scientific/spatial data.
• Solid Python programming skills and working knowledge of ML libraries such as PyTorch, TensorFlow, or scikit-learn.
• Foundational understanding of spectral data and willingness to build deeper hyperspectral expertise on the job.
• Familiarity with core geospatial data handling: raster/vector formats, coordinate reference systems, and tools such as Rasterio, xarray, GDAL, or PyProj.
• Understanding of fundamental ML concepts and willingness to learn domain-specific methods on the job.
• Comfortable working with large raster/imagery datasets and basic cloud or HPC compute environments.
• Strong communication skills and eagerness to learn from and collaborate closely with senior scientists.

Benefits

• Health insurance coverage
• Unlimited leaves & flexible working hours
• Role-based remote work and work-from-home benefit
• Relocation assistance
• Professional Mental Wellness services
• Employee Stock Options for all hires