Role: Junior Data Scientist - Machine Learning (Remote Sensing)
Location: Singapore
We are partnering with a leading climate-tech company that is seeking a Junior ML Scientist who loves turning messy satellite data into clean, production-ready models. You will work closely with our ML and engineering teams to build, test, deploy, and maintain machine-learning pipelines for climate intelligence and remote monitoring. This role is ideal for someone early in their career who wants real ownership, fast learning and the chance to ship models used by insurers, governments and infrastructure operators.
Responsibilities
- Implement, train, and validate ML models for optical and radar satellite imagery.
- Build clean, modular training and inference pipelines (PyTorch/TensorFlow).
- Work with engineering to deploy models into production (Docker, CI/CD).
- Support data preparation: tiling, preprocessing, augmentation, metadata cleaning.
- Write tests (unit + integration) and maintain model performance dashboards.
- Collaborate with senior scientists on R&D tasks such as model improvement, feature engineering, and synthetic data workflows.
- Run experiments, document results, and communicate findings clearly.
Requirements
- MSc in Machine Learning, Computer Vision, Remote Sensing, Data Science or related field OR strong project/portfolio experience.
- Solid programming skills in Python, with experience using PyTorch or TensorFlow.
- Understanding of convolutional models, transformers/attention or change-detection techniques.
- Some exposure to geospatial workflows (e.g., Rasterio, GDAL, QGIS) or willingness to learn quickly.
- Basic experience using Git, Docker, or cloud environments (AWS/GCP).
- Comfort working with large datasets and debugging data issues.
- Good communication and eagerness to learn.
Nice-to-Have
- Hands-on experience with satellite or aerial imagery projects.
- Familiarity with SAR data or multi-modal fusion.
- Experience pushing models to production (CI/CD, containers).
- Basic understanding of ONNX, model optimisation, or GPU workflows.
- Experience with synthetic data generation or augmentation strategies.
- Curiosity for climate resilience, environmental monitoring, insurance or earth observation.
