Christian Lorentzen
Chief Reserving Actuary Non-Life at ETH Zürich
Bern, Bern, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolChristian Lorentzen is Chief Reserving Actuary Non‑Life at Die Mobiliar with nine years of actuarial and data-science experience overseeing reserving, capital and catastrophe modelling across all non‑life risks. He blends deep quantitative rigor from a physics and PhD‑level background with practical insurance expertise, having progressed from pricing actuary roles to leading dynamic modelling teams. An active scikit‑learn core contributor and open‑source collaborator on SciPy and NumPy, he brings production‑grade statistical and numerical improvements to widely used scientific libraries. He also teaches responsible machine learning at ETH Zürich, linking academic clarity to industry practice. Known for improving edge‑case robustness in statistical code, he focuses on making probabilistic models both accurate and interpretable for business stakeholders. Based in Bern, he combines hands‑on coding, technical leadership and regulatory-savvy actuarial judgement.
9 years of coding experience
Fully Qualified Actuary SAV, Actuarial Science, Fully Qualified Actuary SAV, Actuarial Science at University of BernDiplom (equivalent to M.Sc.), Physics, Diplom (equivalent to M.Sc.), Physics at Karlsruhe Institute of Technology (KIT)Master's degree, Physics, Master's degree, Physics at Université franco-allemande Deutsch-Französische Hochschule UFA DFHBinational double degree awarded by the Franco-German University, Physics, Binational double degree awarded by the Franco-German University, Physics at Université Joseph Fourier (Grenoble I)
German, English, French
Github Skills (29)
algorithm10
algorithms10
scipy10
learn-ruby-on-rails10
pytest10
python10
scikit10
testing10
user-manual10
machine-learning10
basics10
numpy10
tensorflow10
f10
statistical-models10
Programming languages (9)
Github contributions (5)
scikit-learn/scikit-learn
Aug 2020 - Jan 2023
scikit-learn: machine learning in Python
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:1846 reviews, 77 commits, 435 PRs in 2 years 5 months
Contributions summary:Christian made significant contributions to improving the statistical models within the scikit-learn library. Their work involved fixing and testing loss functions, particularly related to binomial deviance and addressing edge cases in those calculations. The user implemented and tested new statistical methods, adding features like a new splitting criterion and incorporating sample weights into existing estimators, demonstrating expertise in statistical modeling and algorithm design. They are also responsible for improving the accuracy and consistency of predictions by correcting and improving the existing implementations.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
scipy/scipy
Jan 2021 - Nov 2022
SciPy library main repository
Role in this project:Back-end Developer & Documentation Specialist
Back-end Developer & Documentation SpecialistContributions:157 reviews, 14 commits, 20 PRs in 1 year 11 months
Contributions summary:Christian primarily contributed to the SciPy library by adding and improving mathematical functions. Their work included implementing Wright's generalized Bessel function and the `log_wright_bessel` function, indicating a focus on extending the library's numerical capabilities. Furthermore, the user revised the documentation, adding examples and clarifying existing docstrings for enhanced user understanding. They also made minor code improvements, showing attention to code quality and maintainability.
scipypythonscientific-computing
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.