Climate Hazard Modeler - Tropical Cyclone
Risk Management Solutions (RMS) is the world's leading provider of mathematical models and information related to the financial impact of natural catastrophes. Our Model Development department has over fifty Ph.D. scientists and engineers based in London, building mathematical models that predict the distributions of possible damage due to the effects of tropical storms, extra-tropical storms, thunderstorms, storm-surges, and freshwater floods. To that effect, we use observational and reanalysis data, as well as numerical, statistical, data science, and engineering models.
We are the pioneers in the development and application of cutting-edge, combined statistical and numerical modelling methods for the quantification of natural hazard risk, and our models are the most detailed and comprehensive models of natural catastrophes produced anywhere in the world.
Our clients include several hundred insurance and reinsurance companies as well as brokers, banks, hedge funds, regional and local governments, and multilateral agencies.
Objective of the Role
This role involves researching the hazard of tropical storms and developing statistical and mathematical models which quantify the risk they pose to society. It presents a unique opportunity to work in a multidisciplinary team of catastrophe modelers including statisticians, mathematicians, physical scientists and engineers, to build models which are critical to our Re/Insurance clients and essential to the other markets which RMS serve.
The successful candidate will perform research and development work in tropical cyclones and their extreme behavior, with the overarching goal to quantify their loss potential. S/he will work closely with hazard, vulnerability, exposure, and financial modelers to ensure the accuracy, efficiency and appropriateness of the final modelling product.
The ideal candidate’s qualities, skills and attributes follow:
- PhD in a relevant subject (e.g. Atmospheric Science, Statistics, Climate Science, Applied Mathematics, or closely related areas). Holders of an MSc degree in the above relevant subjects, with appropriate work experience may also be considered.
- Strong mathematical foundation with particular focus in numerical modeling, statistics, probability, and computational science.
- Demonstrated success in developing sophisticated mathematical models in academic or industrial environments.
- Experience with climate analyses & data, and in particular understanding of tropical storms processes.
- Experience working with large and complex datasets.
- Strong ability in modelling languages (e.g. R, Python, Julia), and modern systems of scientific repeatability and engineering scalability (e.g. GitHub).
- Experience with high-performance clusters and strong user skill in a Linux/Unix environment.
- Ability to communicate analysis results and insights effectively both internally and with external business partners.
- Demonstrated success to manage priorities and deadlines and to work independently in a highly dynamic and diverse environment with multiple concurrent goals and with a diverse team of scientists and engineers.
- Excellent time management and planning skills with a commitment to delivery.
- Driven and committed, demonstrating initiative and self-motivation.
- Critical thinking and problem-solving skills.
- Attention to detail and intense curiosity.
- Familiarity and interest in the re/insurance business domain.
- Familiarity and interest in state-of-the-art modelling methods including AI and ML.
- Willingness to pursue continued education in support of the role and team goals.
Please provide your CV highlighting the salient education and experience, and a Cover Letter demonstrating how these meet the requirements of the position.
There is a 1% chance an earthquake will cause $50 billion of insured loss within the next 12 months and a 5% chance that a hurricane will cause $60 billion of insured losses next year. At RMS, we turn risks into real numbers. How? By building simulation models that allow insurers and investors to understand and manage their global risks--from hurricanes, quakes, and wildfires, to cyberattacks, terror attacks, and pandemics. Why? We want to build a more resilient world, and we are on a mission to help make every risk known.
Insurers, reinsurers, investors, financial institutions, governments, and NGOs trust RMS solutions to better understand and manage catastrophe risks. RMS was founded in 1989 by Stanford scientists who created our first model for California Earthquake. Today, RMS has some 1,300 employees across 13 offices in the US, London, Bermuda, Zurich, India, China, Japan, Singapore, and Australia, and over 1,000 products and models now covering six continents.
RMS helped pioneer the natural catastrophe model market we now lead – and we continue to innovate. In May 2019, we announced RMS Risk Intelligence™ (RI), an open-standard platform for strategic risk management. Through this purpose-built platform, clients can tap into RMS HD models, rich data layers, intuitive applications and APIs that simply integrate into existing enterprise systems to support business decisions across underwriting, risk selection, mitigation, and portfolio management.
How we understand and manage risk affects everyone and our passion is nothing less than creating a more resilient world through a better understanding of catastrophic events. Join our team of leading scientists, developers, industry experts, and world-class professionals. Together, RMSers make a difference on a truly global scale.
RMS is proud to be an equal opportunity workplace. We are committed to equal employment opportunity without regard to race, color, creed, gender, religion, marital status, registered domestic partner status, age, national origin or ancestry, physical or mental disability, genetic characteristics, sexual orientation, or any other classification protected by applicable local, state, or federal law.
To all recruitment agencies: RMS does not accept unsolicited agency resumes and will not be responsible for the payment of placement fees related to unsolicited resumes submitted to open positions, job aliases, or to our employees.