Kuva Space is on a mission to solve the world's most pressing issues, such as climate change, food security, safety and security, by building the world's most extensive hyperspectral microsatellite constellation and developing AI-driven analytics services.

We deliver reliable and timely global insights and foresight that transform rich spaceborne data into actionable insights customers can use for efficient resource management, optimizing operations, and improving profitability sustainably.

We are seeking a Data Scientist to design, build, and deploy machine learning solutions that extract actionable insights from hyperspectral satellite imagery. You will apply computer vision and remote sensing techniques to develop models that power core geospatial products and analytics. The ideal candidate brings strong Python engineering skills, hands-on Machine Learning/Deep Learning (ML/DL) experience, and fluency with geospatial data and tools.

Data Scientist

Position: Full time

Location: Hybrid / Remote

Key Responsibilities

1. Machine Learning/Deep Learning (ML/DL) Models

Research, prototype, and productionize ML/DL models for remote sensing use cases (e.g., classification, detection, segmentation, regression, anomaly detection) with hyperspectral satellite data
Optimize model performance and scalability on CPU/GPU, including batch inference, quantization/pruning when appropriate, and distributed training (e.g., Dask/Spark/PyTorch Distributed)
Contribute to ML Operations practices: experiment tracking, model/version management, CI/CD for ML, containerization, and cloud-based deployment
Monitor model health and data drift; develop internal tooling for data validation, spectral calibration checks, and automated QA

2. Computer Vision Algorithms

Develop computer vision algorithms for imagery pre-processing, feature extraction, and quality control (radiometric, geometric, atmospheric considerations)
Build robust Python codebases and data pipelines for training, evaluation, and inference; write clean, typed code with unit/integration tests and participate in code reviews

3. Manage and Analyse Datasets

Curate and manage datasets: ingestion, tiling, augmentation, labeling strategies, spectral feature engineering, and metadata management
Analyze and process geospatial data using GDAL/Rasterio, GeoPandas, QGIS, and PostGIS; design efficient SQL queries for large geospatial datasets

4. Collaborate and Document

Collaborate with product, engineering, and domain experts to translate requirements into well-scoped experiments and production-ready solutions
Document methods, datasets, and model behavior; stay current with advances in ML, computer vision, and remote sensing

Qualifications

Master’s degree in Computer Science, Data Science, Remote Sensing/Geoinformatics, Electrical/Computer Engineering, Physics, or related field; or equivalent experience
Strong proficiency in Python and the scientific stack (NumPy, Pandas, SciPy, PyTorch), with experience building maintainable, tested codebases (type annotations, packaging, linting)
Hands-on experience developing and deploying ML/DL models using scikit-learn and one or more deep learning frameworks (PyTorch or TensorFlow); experience with OpenCV
Demonstrated experience with remote sensing imagery and geospatial tooling: GDAL/Rasterio, GeoPandas, QGIS; solid understanding of projections, georeferencing, and raster/vector data
Proficiency in SQL and experience with PostgreSQL/PostGIS for spatial queries and performance tuning
Familiarity with cloud or high-performance computing environments (e.g., AWS/GCP/Azure, Slurm/HPC), containers (Docker), and Linux/bash
Strong problem-solving skills, ability to design rigorous experiments, and communicate results clearly to technical and non-technical stakeholders
Experience working collaboratively in code (Git e.g., GitHub) and participating in peer reviews

Preferred Qualifications

Experience with hyperspectral imaging (sensor characteristics, spectral indices, unmixing, MNF/PCA, spectral libraries, anomaly detection)
Experience with satellite or aerial data pipelines and photogrammetric/RS preprocessing (radiometric/atmospheric correction, orthorectification)
Knowledge of geospatial data formats and standards: GeoTIFF/COG, STAC, Zarr, OGC APIs
Experience with distributed data processing (Dask, Spark) and GPU acceleration; exposure to Kubernetes, serverless, or batch inference workflows
MLOps tooling such as MLflow, Weights & Biases, or SageMaker; experience deploying models to APIs or streaming/tiling services
Background in physics-based modeling, BRDF, or optical/spectral remote sensing

What do we offer?

At Kuva Space, we offer a stimulating and safe work environment that encourages growth, collaboration, and excellence. Constantly learning new things is the norm here! Our focus on space technology means you'll have the opportunity to learn more about satellites, space, data, and the Earth. If you get excited about space-themed lunch table discussions, Kuva Space is the community for you 🪐✨

As an employer, we prioritize the well-being of our employees, both physically and mentally. Our health care benefits are comprehensive, including dental benefits and short-term psychotherapy. We offer an annual sports, culture and transport benefit. And our team members regularly meet up for after-work activities 🧘🏽

If you do not live in Finland but wish to, we can offer assistance in the migration process and support with learning the Finnish language and culture 🇫🇮

In addition, you will be part of a dynamic, fun, and highly skilled team! Our diverse team of international colleagues, with varying cultures, shares a passion for deep tech and making our Earth more sustainable. And we believe Finnish 'sisu' is a must-have mindset to overcome any challenges that come our way 🇫🇮

If you’re excited to apply advanced ML and computer vision to geospatial challenges and build production-grade solutions, we’d love to hear from you! 🧑‍🚀

The application period will be closed on 9.2.2026, after which we will begin reviewing applications and inform on our decision as soon as possible.