Philipp Schiele
Quantitative Developer
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolPhilipp Schiele is a Quantitative Developer at Citadel with six years of experience applying convex optimization, machine learning, and risk modeling to real-world finance problems. He draws on a strong academic foundation—including a PhD candidacy and postdoctoral research at Stanford in Stephen Boyd’s lab—to translate advanced optimization theory into production systems. At Scalable Capital he progressed from intern to team lead, building portfolio optimization and risk tools used in live asset management. As a core maintainer of the widely used CVXPY library, he has contributed both algorithmic fixes and CI/packaging improvements that improved reliability and deployment of convex solvers. Philipp combines research-grade rigor with hands-on engineering, notably integrating Black–Litterman and semivariance methods into open-source portfolio tools. Based in New York, he bridges academia and industry to ship scalable, auditable quantitative systems.
6 years of coding experience
5 years of employment as a software developer
Ludwig Maximilian University of MunichFinance, Finance at Copenhagen Business SchoolBachelor of Science - BS, Business Studies and Economics | International Financial Economics, Bachelor of Science - BS, Business Studies and Economics | International Financial Economics at Universität Konstanz
Github Skills (21)
github-ci10
python10
testing10
cvxpy10
convex-optimization10
cicd10
numpy10
financial-analysis10
optimisation10
githubaction-workflow10
algorithmic-trading10
optimization10
pandas9
build-automation9
quantitative-finance9
Programming languages (5)
Github contributions (5)
cvxpy/cvxpy
Feb 2021 - Jan 2023
A Python-embedded modeling language for convex optimization problems.
Role in this project:
Backend Developer & DevOps Engineer
Backend Developer & DevOps EngineerContributions:11 releases, 343 reviews, 70 commits in 1 year 11 months
Contributions summary:Philipp contributed to the core functionality of the CVXPY library, making modifications to define conditions more precisely and remove debugging assertions, which improved the reliability of the convex optimization modeling language. They also enhanced the usage of NumPy and extended the non-negativity checks within the library. A significant portion of their work involved transitioning the continuous integration pipeline from Travis CI to GitHub Actions, automating testing and deployment processes. Furthermore, the user implemented several improvements for the setup and testing of Gurobi CI and the building of Manylinux wheels.
pythonconvex-optimizationproblemsoptimizationconvex
robertmartin8/PyPortfolioOpt
Nov 2020 - Nov 2022
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Role in this project:Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:10 reviews, 36 commits, 10 PRs in 2 years
Contributions summary:Philipp implemented core functionality for financial portfolio optimization, including the efficient semivariance calculation, efficient risk, and efficient return methods. They focused on integrating the Black-Litterman model and optimizing portfolios using convex optimization with cvxpy. The user also made several improvements to testing, ensuring the functionality of the implemented methods, and adapted the risk model for more accurate calculations.
pythonportfolio-optimizationriskhierarchicalportfolio-optimisation
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