Quantitative Finance
Pricing, risk, structured products, indices, hedging workflows, and market-aware analytical tooling.
Quant Finance • Statistical Modelling • ML Research
Applying modern technology to financial markets.
Quantitative finance work across pricing, risk, market data, and research systems, with a focus on turning statistical ideas and machine-learning methods into practical financial tools.
A professional profile at the intersection of financial markets, statistical modelling, machine learning, and production-grade quantitative systems.
Approach
Research discipline with production awareness.
I focus on financial problems where data, models, markets, and implementation all matter. The work starts with market structure and statistical evidence, then moves toward tools that can be tested, automated, monitored, and used in decision-making.
Machine learning and modern AI are part of that toolkit, especially for research, feature discovery, anomaly detection, forecasting, and model review. The goal is not novelty for its own sake; it is better financial insight and more robust execution.
Statistical Modelling
Econometrics, stress testing, valuation models, anomaly detection, and evidence-led model development.
Machine Learning Research
Applying ML methods to financial data, research workflows, signal investigation, and model validation.
Production Systems
Python, APIs, data flows, automation, cloud infrastructure, and monitoring for financial analytics.
Contact
Start with the market question.
For professional enquiries or project conversations, use the email below.
dimitris@bekos.uk