October 2016
Message 87

[Periods|Index by:DateThreadSubjectAuthor|Date:PreviousNext|Thread:(Previous)(Next)|List Information]

[Met-jobs] Statistician for Cyber Risk Modelling at RMS London

From Christos Mitas <>
To "" <>
Date Wed, 26 Oct 2016 05:07:18 +0000

Risk Management Solutions (RMS) is the world's leading provider of mathematical models and services related to the financial impact of catastrophes. Our Model Development department has over fifty PhD scientists and engineers based in London, building mathematical models which quantify the distributions of possible damage due to catastrophic events, both natural (e.g. earthquakes, storms, floods), and man-made (e.g. cyber threats, terrorist attacks, pandemics).


Our clients include several hundred insurance and reinsurance companies as well as brokers, banks, hedge funds, regional and local governments, and multilateral agencies.


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 shape and expand 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 risk


The successful applicant will assemble and use large and complex datasets to extract and manipulate data for the development of sophisticated risk models.

S/he will use various modelling techniques to quantify the impact of cyber risks (e.g. data breaches, 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.


Due to the dynamic nature of cyber risk, a significant responsibility will be to assist in the growth of the team by understanding the cyber risk landscape and be able to identify trends in areas in which our efforts and expertise will require expansion.


The ideal candidate’s qualities, skills and attributes:


·         PhD degree from a top university in statistics or a relevant subject; for example, applied mathematics, actuarial science, computer science, engineering, or physical sciences.

·         Very strong mathematical foundations with particular focus on mathematical statistics and probability.

·         Experience working on large and complex datasets including ones stored in relational databases.

·         Demonstrated success in developing sophisticated models using advanced mathematics and statistics in academic or industry environments.

·         Strong capabilities in modelling languages such as R and Python.

·         Previous experience in 1) cyber risk assessment and analysis, and 2) modelling economic impacts of disasters.


·         Knowledge and experience with advanced methods of actuarial modelling.

·         Knowledge and experience in Bayesian data analysis.

·         Familiarity or interest in advanced modelling techniques like game theory and agent-based modelling.

·         Strong user skills in a Linux/Unix environment.

·         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.

If you are interested, please apply directly at:, including a cover letter and CV.

Appropriate candidates will be invited for interview in London.


Many thanks,



Christos Mitas, PhD

Vice President of Model Development


Go to: Periods · List Information · Index by: Date (or Reverse Date), Thread, Subject or Author.