Greater Madrid Metropolitan Area
Quantitative Data Analyst and Researcher specializing in econometrics, political risk, and high-performance data pipelines. Leveraging a foundational background in complex systems engineering and applied mathematics, I design scalable algorithms to isolate causal relationships within unstructured and high-frequency macroeconomic datasets. As a dual US/German citizen based in Munich, I am currently doing an exchange semester at IE University in their Master's in Technology and Global Affairs program, while finalizing an M.Sc. in Political Economy at TU Munich, where I lead data-driven research bridging artificial intelligence, NLP, and international political economy. This comes subsequently to my professional work as a software test and systems engineer, as well as my B.Sc in Mechanical Engineering from Purdue University. Key Expertise: - Programming and Scripting - Python (Pandas, Scikit-learn, RapidFuzz, Regex, TF-IDF), R (Tidyverse, sf, Panel Data Regression), C/C++, MATLAB (FFT, POD, SVD). - AI/ML - Large Language Models (LLMs) (Prompt Engineering, Relational Extraction), Social Network Analysis (Graph Theory, Structural Balance), Quantitative Methods (Causal Inference, Hypothesis Testing). - Engineering and Simulation Tools - Hardware-in-the-Loop (HIL) & Software-in-the-Loop (SIL) testing; Model-Based Systems Engineering (MBSE), Cameo Systems Modeler, Azure DevOps, Rally, Git, LabView - Languages and Other- Native/Fluent in English and German. Operator Trainer for John Deere Construction Machinery. Open to roles in Quantitative Macro Research, Economic Data Science, and Political Risk Analysis in the DACH region (specifically Munich and Zurich).
- Architected a PyTorch-accelerated Transformer neural network pipeline to reconstruct a corrupted dataset, fusing Semantic Textual Similarity (STS), rule-based NLP, and the Hungarian optimization algorithm to mathematically resolve matching. - Engineered a high-performance Python pipeline (Pandas, Scikit-learn, RapidFuzz) to process 300,000+ unstructured records, designing a regex-based "waterfall" merging algorithm and utilizing TF-IDF cosine similarity to standardize 6 datasets. - Led applied LLM research for Agentic AI design, engineering prompts and controlled vocabularies to transition from sequential to relational data processing, successfully extracting explicit/implicit social ties and structural network graphs. - Conducted advanced econometric modeling in R (panel data, fixed effects, causal inference) to evaluate macroeconomic trends, collaborating with cross-functional teams (TUM and LMU) to define standard data annotation protocols.
- Architecture system-level logic and control algorithms for prototype Battery Electric Vehicles (BEVs) using MBSE tools (Cameo Systems Modeler), standardizing controller messaging across distributed embedded systems. - Led technical defect investigations utilizing Azure DevOps and integrated legacy high-performance features into EV prototypes to ensure scalable, compliant software solutions in an Agile environment.
- Engineered automated HIL/SIL testing pipelines, writing ECU vector scripts and decoding high-frequency CAN bus data via Vehicle Spy to validate safety-critical software functionality. - Managed the software release lifecycle and established MBSE testing protocols, utilizing Rally to track defects and coordinate with developers on root-cause resolutions for production-ready code packages.
- Developed custom VSpy/VividCAN scripting to automate simulation cycles and timer interfaces, enabling high-fidelity field heat rise testing and data visualization. - Designed experimental data acquisition setups via Dewesoft, modeling system endurance to translate physical thermal capacity tests into actionable engineering data for power electronics optimization.
- Developed MATLAB computational algorithms applying modal decomposition techniques (FFT, POD, SVD) to process high-speed thermal imaging and identify synchronization patterns in chaotic propulsion flow fields. - Designed complex testing infrastructure, including a Mach 2 negative pressure shock tube system and high-fidelity sensor instrumentation, to advance research in laser diagnostics and turbine efficiency. - Prototyped geospatial tracking algorithms and authored Concept of Operations (ConOps) reports for emerging autonomous aviation technologies, earning 2nd Place Nationally in the FAA Airport Cooperative Research Design Competition.