<p>Position: Data Scientist</p><p>Location: Athens, Greece (preferred) | Espoo, Finland | Valencia, Spain | Warsaw, Poland</p><p>Hiring Model: Hybrid (2 days per week remote)</p><h3>Who are we?</h3><p>ICEYE is a Finnish-based, international New Space company and the global leader in synthetic aperture radar (SAR) satellite operations for Earth Observation and persistent monitoring. Headquartered in Finland, we operate from five locations around the world with over 700 employees representing more than 57 nationalities. Our tight-knit team of experts spans engineering, software development, radar technology, and beyond. We’re innovative, driven people who strive for excellence in everything we do. As advocates of our corporate culture, we value teamwork and curiosity—and we know how to have fun!</p><h3>What do we do?</h3><p>ICEYE builds and operates the world’s largest commercial constellation of small SAR satellites, making radar data available to government and commercial partners. Our satellites acquire images of Earth at any time—even through clouds and darkness—delivering unmatched persistent monitoring capabilities. Information derived from our imagery powers data-driven decisions across maritime safety, disaster management, insurance, finance, and other time-critical sectors. Our Data Science team builds models focused on natural-catastrophe prediction, mapping, and the tools that support these analyses. The Tasking Model team develops and deploys optimization algorithms and services that determine where, when, and how to task the constellation—ensuring imagery acquisition aligns with and maximizes the impact of our catastrophe-monitoring solutions.</p><h3>What are we looking for?</h3><p>We’re seeking a mid-level Data Scientist (3–5 years’ experience) with a strong DS/ML and software-engineering background to join our Data Science team. You’ll focus on optimizing satellite tasking models: translating complex business and orbital constraints into scalable algorithms and production-grade services to support our NatCat products.</p><h3>Who You Are</h3><ul> <li>A pragmatic problem-solver who breaks down complex, abstract challenges into clear, implementable steps</li> <li>Passionate about productionizing ML and data science models — comfortable from data ingestion through model evaluation to deployment and monitoring</li> <li>A collaborative team player with excellent communication skills, able to explain trade-offs and findings to both technical and non-technical stakeholders</li> <li>Skeptical and data-driven: you question assumptions, validate with evidence, and drive continuous improvement</li> </ul><h3>Your Responsibilities</h3><ul> <li>Optimization modeling: Design, implement, and refine algorithms that schedule satellite tasks under dynamic constraints</li> <li>End-to-end ML workflows: Build ETL pipelines, perform feature engineering, train and evaluate models for tasking efficiency</li> <li>Software engineering: Writing maintainable code, containerizing algorithms (Docker) and following software engineering best practices (testing, code reviews, etc).</li> <li>API &amp; orchestration: Develop model-serving endpoints (e.g. FastAPI) or integrate with Argo Workflows for automated task execution</li> <li>Cross-functional collaboration: Partner with orbital mechanics experts, meteorology experts, product managers, and software teams to align on requirements and rollout plans</li> <li>Monitoring &amp; iteration: Instrument model performance, analyze real-world outcomes, and iterate to meet evolving mission goals</li> </ul><p></p><ul> <li>Master’s or PhD in Data Science, Computer Science, Engineering, Mathematics, or equivalent</li> <li>3–5 years of applied data-science or ML engineering experience</li> <li>Proficient in Python and OOP principles, with exposure to software engineering best practices, unit testing, version-control (Git), gitflow and code reviews</li> <li>Hands-on experience with SQL for data exploration and pipeline development</li> <li>Familiarity with geospatial and numerical libraries such as geopandas, rasterio, NumPy, PyTorch, and scikit-learn</li> <li>Demonstrated ML expertise across ETL, feature engineering, model training, hyperparameter tuning, and evaluation (deep-learning experience not required)</li> <li>Background in optimization problems (linear/integer programming, heuristics)</li> </ul><h3>Valuable Skills</h3><ul> <li>Experience working with geospatial or remote-sensing data (TLEs, SAR imagery, orbital data)</li> <li>Familiarity with MLOps tools and workflows, including Docker and AWS (Sagemaker, Lambda, ECS/EKS, S3, etc.)</li> <li>Experience designing and deploying model APIs (FastAPI or similar) or workflow orchestration (Argo Workflows)</li> <li>Knowledge of reinforcement-learning and optimization techniques for dynamic scheduling</li> <li>Familiarity with geospatial databases and standards (PostGIS, Parquet, STAC)</li> <li>Experience with monitoring and alerting (Prometheus/Grafana or equivalent)</li> </ul>

ICEYE

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