Meteorologist (MSc University of Zürich/ETH Zürich) with more than 10 years of experience in traditional reinsurance and ILS industry: reinsurance pricing and catastrophe model validation. Development of pricing and portfolio reporting tools. Profound knowledge of statistical methods and software (MySQL/R/Shiny/Leaflet/Markdown).

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  • ILS/Property Cat. Reinsurance
    • Layer pricing tool (data capture, storage, processing and visualisation)
    • Portfolio reporting tool
    • Cat. event and claims database
  • Catastrophe modelling and validation
    • Vendor model validation, model blending/calibration
    • Sensitivity analysis
  • Spatial data analysis
    • Big data handling: retrieval, cleansing, processing
    • Meteorological, climatological data
    • Spatial data handling and visualisation, GIS

Demo Sites

  • R Markdown and Leaflet Report: Seamless integration of R Markdown and Leaflet modules using real-time meteorological data. Demo | Article
  • R Shiny Interface: Sample of an integrated reporting UI (MySQL, R, Shiny) using climatological data. Demo | Article
  • R Shiny Interface: Sample of a reporting and visualisation UI (MySQL, R, Shiny) using real-time meteorological data. Demo | Article

Other Projects

Why I use R

R is the leading tool for statistics, data analysis, and machine learning. It is more than a statistical package; it’s a programming language, so you can create your own objects, functions, and packages. R allows you to integrate with other languages (C/C++, Java, Python) and enables you to interact with many data sources: ODBC-compliant databases such as MySQL, MSSQL (even Excel, Access) and other statistical packages. Over the last ten years I gained extensive experience with R.

Some R packages I use regularly within my projects:

  • data.table for large datasets (“heavy lifting”)
  • parallel for everything wich can be done simultaneously
  • rlang for non standard evaluation (NSE)
  • dplyr and purrr for data processing
  • sf for spatial data processing and manipulation
  • ggplot2 for print-ready complex plots and charts
  • shiny for intuitive UI development
  • rmarkdown for reports (HTML or PDF)
  • leaflet for seamless integration of maps into shiny/markdown projects