Research Scientist

Summary

Join Chelsea Avondale, a leading home insurance group, and contribute to the development of cutting-edge risk modelling and platform technologies. As part of the engineering division, Skynet Software, you will collaborate with a team of scientists and engineers. Your responsibilities will include contributing to the development of severe natural hazard models, championing the development cycle, evaluating scientific literature, and writing scientific code in Python. You will manage project priorities and deadlines, calibrate and validate model components, and research and augment various datasets. This role requires a strong background in applied mathematics or physics, experience with scientific coding in Python, and expertise in mathematical modelling and statistical evaluation. Catastrophe modelling experience is preferred.

Requirements

• BSc, MSc, or PhD in Applied Mathematics or Physics with an outstanding research track record
• Writing high-performance scientific code in Python
• Broad knowledge of mathematical modelling, numerical solutions to differential equations, optimization, uncertainty quantification, Monte Carlo simulations
• Experience with statistical evaluation of large, multi-dimensional datasets
• Demonstrated experience running computer-based experiments with geophysical applications

Responsibilities

• Work with a team of established scientists and engineers with a diverse background in modelling and research
• Contribute to the scientific and technological development of severe natural hazard models (e.g., wildfire, flood, windstorms) and their applications
• Champion the development cycle from data cleaning thru implementation
• Provide critical evaluation of scientific literature and engage industry experts to help formulate solutions to specific problems
• Leverage libraries and packages in Python to write scientific code that is modular, fast and easy
• Manage individual project priorities, deadlines and deliverables with your technical expertise
• Calibration and validation of the individual model components, including benchmarking to historical events
• Research, vet and augment various datasets from different source including remotely sensed satellite imagery, geospatial and environmental information, weather data, with a focus on the need for statistical credibility at all times
• Evaluate model outputs in time and space using GIS tools

Preferred Qualifications

• Experience in academia or industry on related topics post PhD will be beneficial
• Our ideal candidate has catastrophe modelling experience e.g., flood/wildfire/severe weather