Environmental Risk Modeller

Cambridge
1 month ago
Applications closed

Related Jobs

View all jobs

Water Quality Principal Consultant

Senior Data Modeller - Banking & Data Warehouse

Data Modeler

Senior Data Scientist - London

Senior Data Scientist - London

Lead Data Engineer

Environmental Risk Modeller Location: Cambridge, Cambridgeshire (Hybrid)
Salary: 60-85k
Job Type: Full-time, Permanent
About the Role We are seeking a quantitative modeller to support the development of climate physical risk and nature modelling efforts for an innovative and fast-growing organisation. The successful candidate will be responsible for quantifying the impacts and dependencies of businesses on climate and nature, working alongside a talented team of modellers, economists, data scientists, and software engineers.
This is an exciting opportunity for an ambitious individual looking to work on cutting-edge analytics with some of the world’s largest and most forward-thinking corporations.

Key Responsibilities
Develop models using novel techniques to assess risks from climate change and nature, translating complex scientific concepts into quantified financial impacts for businesses.
Write Python code to integrate models into the company’s analytics platform.
Build and analyse geospatial data layers to support risk modelling.
Deliver actionable insights and analytics relevant to corporate decision-making.
Act as a subject matter expert on nature and climate-related risks and opportunities.
Collaborate with internal teams and clients, requiring a minimum of three days per week in the Cambridge office. Essential Skills & Experience
A Bachelor’s degree in natural sciences, physics, engineering, or a related field.
Experience delivering environmental or sustainability-related projects to corporate clients.
Strong knowledge of mathematical modelling, including statistics, geospatial analysis, and probability, with practical applications to real-world problems.
Proficiency in Python (preferred), R, or MATLAB for scientific programming.
Experience working with large geospatial and environmental datasets (e.g., CMIP6, SSPs, or nature data layers).
Strong research skills with the ability to translate data into actionable models and insights.
Excellent communication skills, with the ability to present complex scientific concepts to non-technical audiences.
Ability to work effectively in a fast-paced environment, managing multiple projects while collaborating with a diverse team of scientists and engineers. Desirable Skills
Postdoctoral research experience in a relevant field.
Experience in model development (e.g., natural catastrophe modelling, risk quantification).
Cross-disciplinary expertise in areas such as natural sciences, agronomy, or environmental economics.
Experience quantifying the economic impact of climate and nature-related risks to inform business or government decision-making. Why Join?
Be part of a rapidly growing organisation at the forefront of climate and environmental risk modelling.
Work on high-impact projects with leading global corporations.
Collaborate with a highly skilled team of scientists, engineers, and industry experts.
Competitive salary and opportunities for career progression

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.

Machine Learning Leadership for Managers: Strategies to Motivate, Mentor, and Set Realistic Goals in Data-Driven Teams

Machine learning (ML) has become an indispensable force in the modern business world, influencing everything from targeted marketing campaigns to advanced medical diagnostics. As industries integrate predictive algorithms and data-driven decision-making into their core operations, the need for effective leadership in machine learning environments has never been greater. Whether you’re overseeing a small team of data scientists or spearheading an enterprise-scale ML project, your leadership style must accommodate rapid innovation, complex problem-solving, and diverse stakeholder expectations. This guide provides actionable insights into how you can motivate, mentor, and establish achievable goals for your machine learning teams—ensuring they thrive in data-driven environments.