Flood Data Scientist

Location

Espoo, Finland;
Valencia

Full time

Solutions

• Flood Data Scientist
• Location: Espoo, Finland or Valencia, Spain
• Department: Solutions
• Reports to: Director of Product Engineering
• Employment type: Permanent
• Workplace model: Hybrid
• Employment is subject to applicable security screening (incl. SUPO, where required)

ICEYE leverages proprietary satellite‑derived observations and other auxiliary data to develop geospatial flood risk models, requiring strong hydrological expertise to extract reliable signals.

We need a Flood Data Scientist to bridge the gap between scientific rigor and engineering practicality. You will lead the effort to extract reliable hydrological signals from diverse, often noisy, geospatial datasets to support our expansion across the United States.

This is an outcome‑oriented role: you are responsible for the quality of the features and the integrity of the labels that power our training pipelines. We expect a balance of an experimental mindset for feature discovery and a built‑to‑last discipline for model validation. By utilizing AI‑augmented workflows, you will maintain high‑velocity delivery while ensuring our risk scores remain statistically meaningful and grounded in physical reality.

• 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.

• 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.

• 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.

Our benefits are designed to support your health and wellbeing, at work and beyond. We keep improving them based on employee feedback, and offerings vary by location. Talent Acquisition will confirm what applies for this role and location during the process.

We’re committed to fair, inclusive hiring and equal opportunity. Everyone is welcome to apply. If you need any adjustments or support during the recruitment process, tell us—we’ll do our best to help.