Hire the Best Quantitative Analysts
in Germany

I help research teams and technical founders build and validate reliable, testable scientific and quantitative systems — research prototypes, backtesting frameworks, production execution systems, simulation engines, and performance-critical pipelines. I also conduct independent technical review of quantitative models and research frameworks. Whether you are architecting a new system from the ground up, battling computational bottlenecks, auditing an unpredictable model, or scaling a fragile prototype into production, I can work with you or your team to engineer a robust, high-performance solution. Background: PhD Physics (NYU, 2018). Quantitative Research at JPMorgan Chase — derivatives pricing on a 1M+ LOC C++ library. Max Planck Institute postdoc — general-relativistic hydrodynamics on 1,000+ core HPC clusters. 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 and 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, quality control ML and statistical time-series models with proper cross-validation (no leakage) ▸ Scientific Computing & Research Tooling For physics, chemistry, biology, engineering, and any domain where the core problem is mathematical or computational. Custom numerical solvers (finite volume / finite difference, spectral, particle methods) with stability and convergence analysis Optimization engines (Bayesian, gradient-based, evolutionary) for experimental design, formulation, and parameter search Simulation frameworks from prototype to production grade Scientific data pipelines: ingestion, transformation, quality control, reproducible workflows Verification and validation: benchmarks, unit / 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 Implementation review against specification: numerical stability, edge cases, code-to-spec fidelity ▸ HPC & Performance Engineering Distributed computing (MPI / OpenMP / CUDA), GPU optimization, memory and I/O tuning 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 work — research prototypes turned into tested, reproducible production code; models reviewed against their own claims - De-risking — failure modes surfaced early (leakage, overfitting, edge cases, scaling bottlenecks) - Maintainability — clean architecture, docs, 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 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 against specification - Market data pipelines and reproducibility upgrades - Performance optimization of slow Python / C++ codebases - Distributed training, GPU optimization, and inference serving for ML workloads - Custom scientific or industrial simulation and numerical software - Internal R&D tooling for research labs and technical teams If this sounds like a fit, message me with a brief on your current setup and success criteria — I'll let you know how I can help.

Penn Risk Management graduate with >4 years of experience in financial analysis. My professional experience in the financial sector is complemented by quantitative finance research conducted at The Wharton School. Avid problem-solver, having placed second in the Ivy-League consulting competition 2023. Driven by the desire to learn quickly, fluent in five languages.

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.

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.

You can find me online by searching Nejc Znidar in your favorite search engine with more detailed description about me, my skills and my past. These days the world is becoming more automatized and AI is gaining power. Moreover businesses rely on statistics more and more. If you are looking for a help at analytics, mathematical and statistical programming, I am here to help you. I have strong mathematical background (studied financial mathematics and quantitative finance). I can organize the data, perform statistical analysis and make easy to understand visualization of the results and data. I am working as a data scientist and I keep expanding my knowledge and skill every day. As such, you will find me constantly programming and improving my statistical approaches to a given problem. I can help you best if you have a problem that you don't know how to solve. I bring business mindset to all analytical and data skills that I have. Also I've invested tens of thousands in my education in different European universities that were at the top of their league in specific knowledge. I can help you if you with any of the following tools (and some more): Microsoft Excel, PowerBI, Tableau, R (for example caret, Random forest and similar packages), Python (especially Pandas, Theano, Numpy and some other packages), Matlab, AutoIT, Stata, Screaming frog, Microstrategy,...

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.