Data scientist and ML engineer working with data since 2016: deep learning, time-series analysis and statistical modelling, with production systems behind them — FastAPI backends, Docker deployments, PostgreSQL/PostGIS. On the quantitative side: risk modelling (VaR, CVaR, Monte Carlo, stress testing), credit scoring and portfolio analysis. Heavy numerics go to his own Fortran kernels.
Experience
- 2026 – nowAI Analyst, Johnson Electric
- 2020 – 2024Data Scientist / Finance Data Analyst, freelance on Upwork — financial forecasting, stock-price and credit-risk models, ML pipelines for imbalanced data, Power BI and Tableau dashboards for clients in e-commerce, finance and research
- 2017 – 2022Data Analyst, Marko — ETL pipelines, real-time data integration, anomaly detection and automated statistical reporting in Python and R
- 2016 – 2017Data Analyst, Connected Horse — high-frequency sensor data from animal trackers: clustering, HMMs and time series to detect behaviour shifts
- 2017 – 2018Veterinarian, veterinary consultancy
Education
- 2026MSc Computer Science — FinTech, Eötvös Loránd University (ELTE)
- 2022 – 2024BSc Computer Engineering, Budapest University of Technology and Economics
- 2024BSc Information Systems and Technologies, Russian State Agrarian University (Timiryazev Academy)
Certifications
IBM Data Science · IBM Data Analyst · IBM Generative AI Engineering · Applied Data Science with R · Practical Time Series Analysis · Financial Markets · Investment Management (University of Geneva) · Applied Bioinformatics (UCSF)
