Within Model Development, the Cyber Risk Modelling team is tasked to develop world-class risk models for the emerging cyber threats, thus providing comprehensive solutions to our Clients. The purpose of this role is to develop tools and capabilities to understand organisations’ cyber risk, and to develop metrics and models to quantify the risk which cyber events pose to an insurance portfolio. It presents a unique opportunity to work on novel modelling ideas and methods which can have a real impact in the medium and longer term development of cyber risk markets. This is further enabled by RMS’ central position as a lead provider of scientific understanding and quantification of catastrophic digital risk.
Essential Job Functions
- The successful applicant will assemble and use large and complex datasets to extract and manipulate data during the development of sophisticated risk models.
- S/he will use various modelling techniques to quantify the impact of cyber risks (e.g. data breaches, contagious malware, DDoS, financial theft, extortion, and so on) to organisations. In addition, s/he will be expected to contribute to team discussions on development of cyber catastrophe modelling methods.
- Understand data sources of cyber threat incidents and assess their quality and quantity.
- Collect, cleanse, organize and curate relevant data sets
- Apply appropriate modelling methodologies to extract conclusions on identity, taxonomy, frequency, severity and interdependency of cyber threats
- Establish iterative workloads for efficient and scalable ETL and modelling
The ideal candidate’s qualities, skills and attributes follow. Please provide your CV highlighting the salient education and experience, and a cover letter demonstrating how these meet the requirements of the position.
- MS/BS/BTech degree in a relevant subject; for example, applied mathematics, computer science, data science, statistics, actuarial science, engineering, or physical sciences.
- Strong mathematical foundation with particular focus on mathematical statistics and probability.
- Experience working on large and complex datasets including ones stored in relational databases.
- Demonstrated success in analyzing volumes of data using ML/DL/NLP techniques.
Strong working knowledge using Ptython/ R.
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. Willingness to pursue continued education in support of the role and team goals.
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