Hire the Best Quantitative Analysts
in Germany

I help research teams and technical founders build reliable, testable scientific and quantitative software — from research prototypes and backtesting frameworks to production-grade execution systems, simulation engines, and performance optimization. I also conduct independent technical review of quantitative models, frameworks, and research. If you're dealing with slow code, messy data, a model you can't trust, or a research prototype that needs to become production, I can turn it into a robust, reproducible system. Credibility: PhD Physics (NYU, 2018). Quantitative Research at JPMorgan Chase (derivative pricing, 1M+ LOC C++ library). Max Planck Institute postdoc (general relativistic hydrodynamics simulations on HPC clusters, 1000+ cores). 13 peer-reviewed publications, 1,300+ citations, h-index 12. WHAT I DELIVER ▸ Quant & Options Engineering Backtesting frameworks (event-driven or vectorized), walk-forward, leakage checks Options analytics: Greeks, IV surfaces, Black-Scholes / numerical methods Research → production pipelines (clean architecture, tests, logging, monitoring) Execution integrations (e.g., IBKR) and robust order/risk handling Market data ingestion, cleaning, corporate actions handling, quality control checks ML and statistical models for time-series signals (proper time-series CV, no leakage) ▸ Scientific Computing & Research Tooling Custom software and research tooling for research-driven teams across physics, chemistry, biology, engineering and beyond — where the underlying problem is mathematical or computational. - Custom numerical solvers (finite volume / finite difference, spectral methods, particle methods), with stability and convergence analysis - Optimization engines (Bayesian optimization, gradient-based, evolutionary) for experimental design, formulation, and parameter search - Simulation frameworks for physical, chemical, and biological systems — from prototype to production-grade - Scientific data pipelines: ingestion, transformation, quality control, reproducible workflows - Verification and validation: benchmarks, unit tests, regression tests, convergence studies - Analysis tools, dashboards, and reporting infrastructure for research workflows - Air-gapped and reproducible deployments where IP sensitivity or regulatory context requires it ▸ Quantitative & Mathematical Review (NDA-protected) Independent technical review of quantitative models, frameworks, and research Verification of internal consistency, identifiability, hidden assumptions, and mathematical correctness Assessment of whether the formal structure supports the conclusions drawn from it Review of implementation against specification: numerical stability, edge cases, and code-to-specification fidelity ▸ HPC & Performance Engineering Performance engineering for large-scale scientific and ML workloads Distributed computing (MPI/OpenMP/CUDA), GPU optimization, memory and I/O tuning, parallel architectures, inference and training infrastructure at scale. Profiling, refactors, and speedups for codebases that need to run reliably under production load. WHY CLIENTS WORK WITH ME - Trustworthy engineering: I turn research prototypes into tested, reproducible production code - De-risking: I find failure modes early (leakage, overfitting, edge cases, scaling bottlenecks) - Maintainability: clean architecture, docs, and handover-ready delivery your team can extend - Communication: clear milestones, concise updates, realistic timelines — no surprises - Math ↔ engineering bridge: strong intuition for both theory and implementation details IDEAL PROJECTS - Quant strategy development, backtesting and research infrastructure - Options analytics and derivatives tooling - Mathematical review of quantitative manuscripts, white papers, or research frameworks - Independent validation of production models or implementations against specification - Market data pipeline and reproducibility upgrades - Performance optimization of slow Python/C++ codebases - Distributed training, GPU optimization, and inference serving for AI/ML workloads - Custom scientific or industrialized simulation or numerical software - Custom internal R&D tools for research labs and technical teams If this sounds like a fit, message me with a detailed brief with your current stack and success criteria — I'll let you know how I can help.

As a Data Scientist at the DKFZ German Cancer Research Center, I combine statistics, machine learning, and my CERN research experience in evaluating and interpreting complicated medical data. I have employed modern approaches like dimensionality reduction, neural networks and Bayesian inference to quantify and predict the effects of different treatments and interventions for Anorexia Nervosa. I have almost six years of expertise in particle physics, detectors, and analytic methodologies, having worked as a Research Assistant at Universität Siegen and a Summer Student at CERN. I am now completing my dissertation on the production cross-section of the high-energy particle process (tt̄γ), referencing the ATLAS detector at CERN. I am fascinated by nature's phenomenology and am eager to learn more about image analysis, MATLAB, and SQL. Apart from physics, I'm also interested in psychology, philosophy, and neuroscience.

Services: - Development of algorithmic trading systems for a wide range of assets including equities, futures, options, crypto and forex - Automation, backtesting and quantitative research - Evaluation, optimization and consulting to improve investment strategies such as avoiding backtest overfitting and other common pitfalls - Creation of customized indicators, factors, signal services and market scanners including automated notifications - Development and enhancement of framework modules such as execution algorithms (including TCA) and portfolio optimization models - Increasing the efficiency of trading algorithms so that large amounts of streaming data can be processed faster and less hardware resources are required Technologies & Tools: - Python including a wide range of libraries like pandas, numpy, numba, scikit-learn, matplotlib, seaborn and many more - QuantConnect / LEAN - Git and GitHub for version control and collaboration - Supported brokers for live and paper trading: Interactive Brokers (IB), Tradier, Binance etc. Bio: Quantitative trader and mathematician with strong affinity to financial markets and coding. Extensive experience in design, development and analysis of algorithmic trading strategies. I have been trading various financial instruments (equities, futures, options and currencies) for more than 10 years, whereas my approach has become more and more systematic over the years. I graduated with a Master's degree in Mathematics at University of Technology Dortmund (Germany) in 2016. My master thesis is about Schroedinger asymptotic of wave equations in periodic media. The subject belongs to the field of nonlinear partial differential equations and has applications in signal transmission using fiber optics technology. I then worked as an IT Consultant for 3+ years and was mainly employed as a data warehouse engineer and ETL developer in the regulatory reporting department of large financial institutions. I currently work full-time as a professional Quant Developer and have already invested many thousands of hours in R&D of financial markets and systematic trading strategies, building a deep knowledge of quantitative finance and algorithmic trading. Other remarks: My algorithms are always professionally designed and well documented, making them easy to follow. I mostly use the framework approach, so that the code is modular, pluggable and clearly structured, which also makes it much easier to perform adjustments or extensions. I am willing to sign a NDA, if desired, so that the intellectual property (IP) rights remain with you.

I am a certified financial analyst with 3 years of experience working as a Financial Software Engineer, responsible for optimizing performance for high-volume financial transactions. My expertise lies in self-autonomous work to deliver high value in areas such as: - Creating algorithms for financial calculations, risk assessment, or trading - Automating tests for financial software systems - Building secure payment processing systems and integrations - Integrating with third-party financial services and APIs - Identifying bottlenecks in process architectures and coming up with scalable solutions I work hard to align myself with the client’s actual goals and objectives for the project, that involves clear communication and the ability to ask the right questions, and great attention to the nitty-gritty. If that’s what you’re looking for shoot me a message and let’s talk.

Algorithms & Optimization Engineer with an M.Sc. in Economics, specializing in simulation, predictive analytics, and decision optimization. Skilled in Python, R, SQL, GAMS, GIS, and IBM ILOG CPLEX, applying advanced algorithms and machine learning methods to solve business and policy challenges. Experienced in building machine learning models, optimization frameworks, and data-driven decision tools across different sectors. Passionate about leveraging evidence-based decision-making for businesses and policy, continuously upskilling in new technologies. I am an economist and business development expert with a solid background in grants, market research, and B2B sales. With a Master's in Economics and a Bachelor's in Engineering, I specialize in economic modelling, sustainability, and the water-energy-food nexus.