Risk Management Solutions Hiring for Modeler at Noida, India Full Time

Modeler

Model Development/Modeling Noida, India

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Overview

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 fifty PhD scientists and engineers based in London, building mathematical models that predict damage caused by tropical storms, extra-tropical storms, thunderstorms, storm surges, freshwater floods, and tsunamis.

We use a combination of observed data, reanalysis data, numerical, statistical and engineering models and data assimilation. We are the pioneers in the development and application of complex statistical and numerical modelling methods for the quantification of natural hazard risk, and our risk 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:

We are currently recruiting for a role related to the modelling of storm-surge, waves, overtopping and coastal flooding in various parts of the world. The modelling work carried out by this team encompasses all steps from hazard modelling to loss modelling. The department has an engaged working environment with a clear scientific, motivated, and technological atmosphere.

Key Responsibilities & Deliverables:

• Building and developing the storm surge & wave model components

• Evaluating model results (both hazard and loss) by comparison to observed data and loss benchmarks

• Further advancement of modules by researching and implementing novel scientific techniques

• Communicating research to clients and other stakeholders

  Requirements:

• PhD in a field related to physical oceanography, geophysical flows, storm-surge or other aspects of coastal ocean modelling. Strong candidates with a relevant MSc and appropriate research or work experience would also be considered

• Experience working with high-performance clusters in a UNIX or Linux environment

• Experience with either numerical models (such as AGCMs, OGCMs, CGCMs, regional models or mesoscale models), handling large data sets, or statistical modelling

• Strong programming ability in scientific and analytical programming languages (e.g. R, Python, Fortran)

• Strong analytical skills and ability to effectively communicate insights to internal and external stakeholders

  Advantageous:

• Experience in implementing, developing, or optimizing code for modelling of geophysical flows would be a strong advantage

• Previous experience in catastrophe modeling, or specific expertise in modelling natural hazards is valued but not necessary

• About RMS, A Moody’s Analytics Company Risk Management Solutions, Inc. (RMS) models and solutions help insurers, financial markets, corporations, and public agencies evaluate and manage global risk throughout the world. RMS has some 1,300 employees across 13 offices in the US, London, Bermuda, Zurich, India, China, Japan, Singapore, and Australia, with products and models covering six continents.

• We lead an industry that we helped to pioneer—catastrophe risk modeling – and continue to innovate. RMS was founded in 1989 by Stanford scientists who created our first model for California Earthquake. Today Insurers, reinsurers, trading companies, and other financial institutions trust RMS solutions to better understand and manage the risks of natural and human-made catastrophes, including hurricanes, earthquakes, floods, terrorism, and pandemics.

• We got acquired by Moody’s Analytics in 2021, opening fascinating new opportunities to expand our models, data, technology and services to industries beyond Insurance to help the world better manage its risks.

• 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.