Senior Data Scientist, Surfline Coastal Intelligence

Surfline Wavetrak
Plymouth, United Kingdom
8 months ago
Applications closed

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Senior Data Scientist

AJ Bell M53Ee, United Kingdom
Posted
1 Feb 2026 (8 months ago)

As a Senior Data Scientist on the Surfline Coastal Intelligence (SCI) team, you will play a central role in developing the next generation of coastal monitoring and prediction technology. Your focus will be on applying advanced computer vision (CV) methods to our global camera network and integrating these outputs with physical, hydrodynamic, and statistical modeling to deliver actionable insights for coastal managers, engineers, and public safety agencies.


You’ll contribute to our technical roadmap, own the delivery of high-value modeling and CV initiatives, and collaborate deeply with data science, engineering, and product teams. This role requires strong analytical and quantitative skills, creativity in solving open-ended problems, and the ability to build production-ready models that perform in challenging real-world environments.


Under Surfline's "Work from Anywhere" policy, this position may be performed from anywhere in the UK.


What You'll Do:

  • Prototype and validate new coastal monitoring capabilities, contributing directly to SCI’s R&D roadmap in collaboration with our Data Science, Engineering, and Product teams.
  • Conduct hypothesis-driven experiments, including feature engineering, model evaluation, and validation against ground-truth datasets (e.g., lidar, surveys, tide gauges, and sensor networks).
  • Work cross-functionally with SCI stakeholders to ensure model outputs meet operational accuracy and delivery requirements for government and enterprise customers.
  • Communicate directly with customers where necessary to gather requirements throughout the product lifecycle.

What We're Looking For:

  • Extensive experience working with Python in research and production environments.
  • Delivery-oriented, able to lead and execute modeling efforts from start to finish.
  • Thorough understanding of the challenges of building ML pipelines with large datasets and taking them from R&D into production.


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