National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Scientist (Climate & Geospatial)

ZipRecruiter
London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist - £80,000 - Hybrid - London

Job Description

Data Scientist – Climate & Geospatial | Financial Services | London (Hybrid)

Location:London, UK

Employment Type:Full-time | Hybrid

We're looking for a talentedData Scientist / Senior Data Scienctistwith a passion for climate risk and geospatial data to join our clients growing analytics team in London. If you're excited about turning complex environmental datasets into actionable insights for the FS sector, we want to hear from you.

About the Role

In this role, you'll work at the intersection ofclimate science, geospatial analysis, and insurance risk modeling, helping their clients better understand and manage the impact of physical climate risk. You'll be building scalable models and tools that directly support underwriting, portfolio risk management, and strategic planning in a changing climate.

What You'll Do

  • Analyse and model climate and natural catastrophe datasets (e.g. flood, wildfire, storm, sea-level rise)
  • Work with large-scalegeospatial data(satellite imagery, GIS layers, remote sensing)
  • Apply machine learning techniques to identify risk patterns and trends
  • Develop tools to visualise and interpret climate risk data for technical and non-technical audiences
  • Collaborate with insurance and reinsurance clients on climate-related risk assessments
  • Stay on top of the latest climate science and ESG regulations impacting the FS industry

What We're Looking For

  • Experience indata science, ideally in climate, geospatial, or catastrophe risk
  • Proficiency inPython,R, or similar, with experience using libraries E.G pandas, scikit-learn
  • Experience with climate models (e.g. CMIP6, ERA5) or catastrophe models is a strong plus
  • Degree in a quantitative field: data science, climatology, environmental science, geoinformatics, or similar

Why Join Us?

  • Be part of a mission-driven team tackling real-world climate challenges
  • Work with industry-leading datasets and tools
  • Flexible hybrid work model (central London office)
  • Competitive salary, bonus, and benefits package
  • Career growth opportunities in a rapidly expanding area of climate risk analytics

#J-18808-Ljbffr

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.

Machine Learning Jobs UK 2025: 50 Companies Hiring Now

Bookmark this page—we refresh the Hotlist every quarter so you always know who’s really scaling their ML teams. The UK’s National AI Strategy, a £2 billion GenAI accelerator fund and a record flow of private capital have kicked ML hiring into overdrive for 2025. Whether you build production‑grade LLM services or optimise on‑device models for edge hardware, employers need your skills now. Below you’ll find 50 organisations that advertised UK‑based machine‑learning vacancies or announced head‑count growth during the past eight weeks. They’re grouped into five quick‑scan categories so you can jump straight to the type of employer—and mission—that excites you. For each company we list: Main UK hub Example live or recent vacancy Why it’s worth a look (stack, impact, culture) Search any employer on MachineLearningJobs.co.uk to see real‑time adverts, or set a free alert so fresh openings drop straight in your inbox.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.