About the job
RMS is the world’s leading provider of analytics and decision science solutions for the quantification and management of catastrophic risks throughout the world. RMS models and services are used by hundreds of insurance and reinsurance companies, hedge funds, corporations, and governments to assess a wide range of natural and man-made perils, from earthquakes and hurricanes to terrorism and disease pandemic.
We are seeking a data scientist to join our Data Analytics and Solutions team in the Model Development group. The team primarily focuses on modeling and developing data and applications related to insured property exposures. Such products are an important part of RMS product line and provide a wealth of valuable information for our clients.
The candidate would be engaged in the development, design and implementation of a wide range of data products used in catastrophe risk modeling, exposure modeling, valuation modeling and Property & Casualty data quality assessment and enhancement. The candidate will be expected to gain and further utilize an in-depth knowledge of the products and relevant technologies.
The incumbent of this role will be primarily responsible for:
- Building CNN or deep learning models to extract relevant features from imagery with high accuracy
- Researching and mining data from structured and unstructured sources to develop and improve our exposure data products
- Developing and optimizing data collection systems using efficient statistical strategies
- Applying machine learning and predictive modeling techniques to develop and improve exposure and other data products
- Identifying, analyzing, and interpreting trends in complex data using statistical techniques
- Working with stakeholders to gather understand and prioritize business and information requirements
- Identifying and executing process improvement opportunities
- Degree in Computer Science, Mathematics, Computational Linguistics, or similar field with experience in deep learning. Ideal candidate would have 2+ years
- Demonstrated experience in applying CNN to real world problems
- Experience in ML techniques, such as supervised or semi-supervised learning clustering, classification, feature engineering, information extraction and so on
- Experience applying NLP techniques for text representation, extraction, and modeling by finding and implementing the right algorithms
- Programming skills in e.g., Python or statistical packages e.g., R or similar
- Knowledge of statistics
- Familiarity with database management systems like SQL would be an added advantage
- Excellent written and verbal skills, as evidenced by white papers or technical presentations at meetings and conferences
- Detail-oriented and self-driven individual
- Outstanding analytical and problem-solving skills
- Ability to independently scope, manage and drive projects
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 up 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.