Philipp Hoffmann
Manager at d-fine
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolPhilipp Hoffmann is a manager and software engineer with 10 years of experience building distributed derivative valuation systems for major banks, combining quantitative rigor from a physics background with software engineering expertise from an Oxford MS. At d-fine he progressed from consultant to manager, leading delivery of production-grade risk and valuation platforms and bridging quantitative models with scalable backend implementations. He contributes to pandas core, improving DataFrame and GroupBy behaviors and performance—evidence of hands-on work maintaining data integrity in widely used open-source tooling. Based in Berlin, Philipp blends research experience from TU Dortmund and UW–Madison with practical consulting delivery, often focusing on correctness and performance in data-heavy financial systems.
10 years of coding experience
Master of Science - MS Software Engineering, Master of Science - MS Software Engineering at University of OxfordMaster of Science - MS Physics, Master of Science - MS Physics at TU Dortmund University
Stackoverflow
93reputation
8kreached
0answers
12questions
Github Skills (16)
data-analysis10
pandas10
debug10
python10
data-science9
testing9
numpy8
ear6
java6
criteria-api6
eclipse6
coding-style6
ftp6
apache6
regex6
Programming languages (1)
Github contributions (5)
pandas-dev/pandas
Feb 2024 - May 2024
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:
Backend Developer
Backend DeveloperContributions:22 reviews, 15 PRs, 59 comments in 2 months
Contributions summary:Philipp primarily contributed to bug fixes and enhancements within the pandas library, focusing on the core functionality of DataFrame and GroupBy operations. Their work involved addressing issues related to `apply`, `join`, and other DataFrame methods, ensuring correct behavior, and maintaining data integrity. They also improved holiday observance rules and addressed encoding issues for file handling. Performance improvements were also made to the numpy datetime benchmarking.
pythondatalabeled-datamanipulationdataframes
dontgoto/pandas
Mar 2024 - Apr 2024
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Contributions:85 pushes, 17 branches in 1 month
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