Benjamin Jonen
Head Financial Engineering at Swiss Life Asset Managers
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolBenjamin Jonen is Head of Financial Engineering in Zurich with 12 years of experience blending quantitative research, software engineering and asset management. He built and productionized multi-asset tactical strategies, ALM/SST optimizations and bond analytics at Swiss Life, and led initiatives to containerize CI pipelines and integrate data science workflows with IT. A PhD in Finance from Universität Zürich and prior developer work on GC3Pie reflect a strong computational background and taste for parallel, automated simulation and optimization. Benjamin also contributes to pandas, improving time-series offsets and performance for custom business day calculations—an uncommon open-source signal for a senior quant. He combines hands-on coding with strategic model governance, especially in credit analytics and ML-based rating models, making him effective at turning research into auditable production systems.
12 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (PhD), Finance, Doctor of Philosophy (PhD), Finance at Universität ZürichMaster's degree, Economics, Master's degree, Economics at Georgia Institute of TechnologyBachelor's degree equivalent, Economics, Bachelor's degree equivalent, Economics at University of Duisburg-Essen
German, English, French
Github Skills (7)
pandas10
python10
data-science10
numpy10
data-analysis10
text-alignment8
text-align8
Programming languages (8)
Github contributions (5)
pandas-dev/pandas
Mar 2014 - Jan 2015
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
Data Scientist
Data ScientistContributions:7 commits, 1 PR, 16 comments in 9 months
Contributions summary:Benjamin contributed to performance improvements within the `pandas` library, specifically focusing on optimizing custom business days calculations. They made changes to the `offsets.py` file, enhancing the efficiency of date calculations related to custom business day logic. Furthermore, the user implemented the `CustomBusinessMonth` offset, adding to the existing time series functionality within `pandas`, and corrected formatting issues in the output. They also addressed a bug in the Series text representation related to displaying the max number of rows.
pythondatalabeled-datamanipulationdataframes
bjonen/pandas
Jan 2015 - Mar 2015
Contributions:21 pushes, 1 branch in 2 months
manipulationmanipulation-librarydata-analysispythondata
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