Quantitative Methods
Modelling markets and risk with statistical foundations and disciplined validation.
Quantitative Finance
Data-driven decisions, built with statistical rigor.
I work at the intersection of financial markets, data analysis and algorithmic decision-making — translating complex problems into robust, transparent strategies you can rely on.
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Finance & Risk
@ Santander
01 — Profile
A measured approach to markets, models and risk.
Currently a trainee in Finance & Risk, I'm fascinated by the intersection of financial markets, data analysis and algorithmic decision-making.
I continuously develop my skills in quantitative methods, Python programming and statistical modelling. My goal: building robust, data-driven trading strategies and solving complex financial problems with analytical precision.
Outside of work, I dive deep into machine learning applications for financial markets, time-series analysis and backtesting frameworks.
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Statistical rigor
Every conclusion is grounded in evidence, validation and honest uncertainty.
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B
Engineering discipline
Reproducible pipelines and clean code that hold up under scrutiny.
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C
Clarity over noise
Complex problems explained plainly, so decisions are made with confidence.
02 — Expertise
Skills & technologies, applied with intent.
Programming & Data
End-to-end systems — from data collection to machine-learning models in production.
Tools & Platforms
A dependable, modern toolchain that keeps work reproducible and well-versioned.
03 — Experience
A record built on trusted institutions.
Mar 2025 — Present
Finance & Risk Trainee
Santander
Asset Based Finance
- Contribution to ongoing ABS transactions, including automation of cashflow and waterfall reports.
Risk Modelling
- Analysis and documentation of credit risk models for internal and external auditors (SAS).
- Statistical validation of scorecard applicability (HCBE).
- Development of an automated scraping tool with GUI and analysis dashboard for dealer verification.
Regulatory Information
- Analysis and preparation of regulatory data for regulatory requirements (SQL, Python).
Aug 2024 — Feb 2025
Data Science Intern
Deutsche Bundesbank
- Development of an end-to-end scraping and data pipeline for automated processing of annual financial statements.
- Built an LLM-based classification model for industry classification of companies (local LLM, API-based).
- Fine-tuning of a deep-learning model for extraction of location and geodata from company reports using self-annotated data.
Research Contribution
Co-authored data pipeline for GeoCSR: Leveraging Geospatial Data from Corporate Reports for Sustainable Finance Insights — in collaboration with LMU Munich and the University of Oxford.
Supporting Research
Contributed to the data pipeline for the GIST Project (Greenhouse Gas Insights and Sustainability Tracking) — a collaboration between Deutsche Bundesbank and LMU Munich.
Sep 2022 — Jul 2023
Teaching Assistant
University of Mannheim
- Tutor for Linear Algebra 1 and Linear Algebra 2A at the Chair of Algebraic Geometry.
- Held weekly tutorial sessions for groups of 20+ students.
- Grading of exams and weekly assignments.
Sep 2021 — Nov 2023
M.Sc. Mathematics for Business and Economics
University of Mannheim
Thesis
Cryptography Based on Ideal Lattices — exploring ideal lattice structures for secure post-quantum cryptographic systems.
- Relevant coursework: Mathematical Finance, Derivatives, Investments, Game Theory, International Asset Management.
04 — Selected Work
Projects where rigor meets engineering.
Featured Project
Financial Data Pipeline
End-to-end system for automated collection, processing and storage of financial market data designed for ML models and derivatives valuation. Features GARCH(1,1) volatility forecasting, cross-validation between data sources and intelligent missing-value handling using Kalman filtering, with multi-layer outlier detection combining statistical methods and Isolation Forest.
TimescaleDBSQLAlchemyyfinanceGARCHKalman FilterIsolation Forest
Featured Project
Trading News Aggregator
Multi-source news aggregation system for financial markets with automated ticker extraction and semantic story clustering. Integrates seven data sources including Finnhub, SEC EDGAR, GDELT and German financial press (FAZ, Handelsblatt), with NLP-based ticker recognition and vector similarity analysis using sentence transformers for story deduplication.
Sentence TransformerspgvectorSEC EDGARAsyncIONLPRSS Parsing
05 — Contact
Let's connect.
I'm always open to discussions about exciting projects, career opportunities or quantitative finance. Feel free to reach out — I'd be glad to hear from you.