Research Scientist

Chelsea Avondale Ltd.; Canada; Remote; CAD 60,000 - 100,000; Full time

Job summary

Join a pioneering insurance group transforming the landscape of home insurance. This role offers the chance to collaborate with top scientists and engineers on cutting-edge projects involving severe natural hazard models. You'll leverage your expertise in Python and mathematical modelling to contribute to innovative solutions that address real-world challenges. With a focus on data-driven decision-making and scientific rigor, this position is perfect for those passionate about making a significant impact in the insurance industry. If you thrive in a collaborative environment and are eager to push the boundaries of technology, this opportunity is for you.

Qualifications

• Experience in writing high-performance scientific code in Python.
• Broad knowledge of mathematical modelling and numerical solutions.

Responsibilities

• Contribute to the development of severe natural hazard models.
• Evaluate model outputs using GIS tools and statistical methods.

Skills

Python

Mathematical Modelling

Numerical Solutions

Statistical Evaluation

Monte Carlo Simulations

Data Cleaning

GIS Tools

Education

BSc in Applied Mathematics

MSc in Applied Mathematics

PhD in Applied Mathematics

BSc in Physics

MSc in Physics

PhD in Physics

Tools

Python Libraries

Geospatial Tools

Job description

Chelsea Avondale is the world’s most cutting edge home insurance group. We have developed the most sophisticated risk modelling and platform technologies for home insurance and deploy that technology through our own insurance company.

Our team consists of some of the brightest minds in scientific risk modelling, systems engineering, and insurance. Our operations include our scientific research & engineering division (Skynet Software) and Canadian property & casualty insurance company (Max Insurance).

Together, our group is working to transform the Canadian and global insurance landscape.

This role will be joining the cornerstone of our organization - our engineering division, Skynet Software.

What you’ll do in this role:

• 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 sources 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.

Must have experience and skills for this role:

• 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.
• 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.

Skynet Software welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.