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Requirements
Your day-to day responsibilities
- Feature Development: Translate environmental, terrain, and hydrological data into model-ready features grounded in physical process understanding; work closely with other hydrological subject matter experts to validate that signals reflect real-world behaviour
- Observation Quality: Assess, extend, and improve the quality of satellite-derived observation labels, investigating noise, bias, and coverage gaps, and designing labelling approaches that reflect confidence in the data
- Experimentation: Design and run experiments to understand what drives predictive performance; distinguish genuine signal from statistical artefact
- Physical Interpretability: Ensure model outputs are explicable in domain terms, the kind of scrutiny that holds up in front of scientists and domain experts, not just benchmarks
- Collaboration: Work closely with Data Engineers to define robust, scalable data and labeling workflows, and with ML Engineers to ensure features, labels, validation criteria, and model evaluation are scientifically rigorous and production-ready
What we’re looking for
Must haves
- Education: Master's degree or higher in hydrology, geomorphology, geoscience, environmental science, civil engineering (water resources), or related quantitative field
- Experience: 5+ years of professional industry experience in data science with geospatial or environmental data, flood modeling, hydrology, climate risk, natural hazard assessment, or remote sensing analytics
- Domain Knowledge: Deep understanding of flood, extreme weather, and hydrological processes; able to reason about what the data represents physically, not just statistically. Familiarity with terrain analysis, catchment hydrology, or drainage network characterisation
- Geospatial Data: Hands-on experience working with raster and vector geospatial datasets, transforming raw environmental data into analytical features
- ML Proficiency: Solid experience with supervised learning for both geospatial and tabular data, training models, interpreting results, and running controlled experiments
- Python: Python-fluent; writes clean, testable analysis code, not just one-off notebooks
- Data Quality: Experience working with observational data that is noisy, spatially biased, or incomplete, and knowing how that affects model behaviour
- Modern Tooling: Pragmatic use of AI tooling (Cursor, Claude, Copilot) for data exploration and analysis acceleration
Nice to haves
- Experience contributing to a shipped product, software engineering practices, version control discipline, code review, CI/CD, not just notebooks and papers
- Familiarity with US government geospatial datasets (FEMA, USGS, NOAA)
- PostGIS, AWS, or Databricks experience
- Remote sensing literacy, SAR, or optical environmental mapping
- Climate risk, insurance, or catastrophe modeling vocabulary
Application Process
- Recruiter screening
- Hiring manager interview
- Technical task
- Technical panel interview
- Final interview
Working at ICEYE
At ICEYE, you’ll join a diverse and highly engaged team united by the ambition to make the impossible possible. As a global scale-up, we combine speed and ambition with the opportunity to take real ownership from day one. Your growth, wellbeing, and success are a priority, with continuous professional development, training opportunities, and a culture where collaboration is how we win.
