Our client is seeking a talented Data Scientist with a geospatial statistics background to play a key role in shaping the future of Ratings product. You will be instrumental in developing innovative methods to quantify climate risk exposure and impact across various hazards. Your work will be highly impactful, informing a wide range of use cases served by the company and directly contributing to the creation of next-generation risk rating products.

Who you are

• Super confident in geospatial statistical methods, including spatial autocorrelation, point pattern analysis, and geostatistics.

• Adept at building and managing ETL pipelines for data transformation and integration.

• Possess proven experience working with spatial climate data and climate modelling outputs.

• A Python pro with proficiency in geospatial libraries like GeoPandas.

• Skilled in using GIS software for data visualization and insightful analysis.

• A gifted communicator with the ability to effectively present complex findings to both technical and non-technical audiences.

• A meticulous problem-solver with a strong analytical mind.

Bonus Points:

• Experience applying machine learning techniques to analyze climate data.

• A solid understanding of climate modelling principles and methodologies.

• Familiarity with risk modelling concepts and practices.

Your Impact

• Design a robust methodology to transform raw climate risk metrics (e.g., flood depth, wind speed) into standardized, comparable risk rating scores. This will allow for apples-to-apples comparison of diverse climate hazards. The methodology should be scalable to accommodate the integration of new data sources.

• Develop a system to generate a single, unified risk rating score that effectively captures the combined impact of multiple climate hazards on a specific location or asset.

• Craft rating methodologies that intelligently leverage real-world damage and loss data to generate more meaningful and actionable risk assessments.

• Collaborate with the team to build efficient, scalable pipelines for generating risk ratings for large datasets, ultimately streamlining their integration into crucial decision-making processes.

Required qualifications

• PhD or Master Degree in Computer Science, Applied Mathematics, Physics, Statistics, Machine Learning, or other data centric disciplines

• 4 years of relevant industry experience.

• 3+ years of practical experience with geospatial (e.g., earth observation) data and hands-on data science roles.

• 4 years of experience in statistical analysis and machine learning.

• Ability to write clean and maintainable Python code & strong knowledge of core Python analytics libraries.

• Strong problem-solving, mathematical and statistical skills.

• Fast learner who is not afraid to get involved in new and challenging areas.

• Team player, flexible, and focused on project/product delivery.