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Kraj: Poland

Lokalizacja: Łomianki, PL

Na Useme od 18 lutego 2026

O mnie

Financial analyst focused on investment modeling and data-driven decision making — with a particular edge in real estate and macro analysis.

My day job is evaluating residential investment properties: building cashflow models, running due diligence, and presenting findings to stakeholders. I bring that same rigor to freelance projects.

Skills I bring to every engagement: — Financial modeling in Excel — cashflow, DCF, forecasting, scenario analysis — Data extraction and analysis using Python and R — Macro and sector research structured for business use — Clear, presentation-ready reports and dashboards

Education: BSc Finance, University of Warsaw | MSc Quantitative Finance (in progress), SGH Warsaw School of Economics.

I take on projects where precision matters and vague inputs need to become clear outputs. Happy to start with a small scoped task so you can see how I work. less

CV / Résumé

Wrz 2024 - Teraz

Senior Financial Analyst

Dobrego Najmu

ricing strategy and acquisition targets are directly driven by my analyses. Cost-per-tenant KPI is a core metric reviewed at every board meeting. Python, R, and Excel/VBA.

Sie 2021 - Lut 2023

Accounting Assistant

Trideo sp. z o. o. sp. k.

Supported day-to-day finance and operations at a small company: data entry and document management, Excel support for non-technical staff, client and office logistics, and working with Symfonia accounting software.

Portfolio

EU__Public_Debt_Analysis__Factor_Analysis__Clustering_Eurostat_Data.docx.pdf

EU Public Debt Analysis — Factor Analysis

Reduced 6 fiscal indicators across all 27 EU member states to two interpretable factors (public sector size and fiscal risk) using principal axis factoring with Varimax rotation. Applied PAM clustering to identify three distinct fiscal models.

Elliptical_Copula__Monte_Carlo_Simulation.pdf

Elliptical Copula & Monte Carlo Simulation

Modeled joint loss distribution across equity, FX, and volatility risk classes using a Student-t copula. Demonstrated that ignoring tail dependence leads to 33% underestimation of VaR at the 99.5% Solvency II threshold. Tools: R.