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oliszymanski/README.md

Oliwier Szymański

Junior BI Analyst | Precision Analytics for Finance and Economics

With a self-taught foundation in Python since age 13 and hands-on experience in financial modeling, I deliver data-driven solutions that uncover hidden patterns and mitigate risks in high-stakes environments. Specializing at the intersection of machine learning, BI visualization, and economic forecasting, I build robust tools—from LSTM networks for FOREX predictions to ML classifiers for loan risk—that enable institutions to make informed, efficient decisions. My recent DataCamp Data Engineer certification (March 2025) and virtual consulting with Accenture underscore a commitment to scalable ETL pipelines and stakeholder-ready insights.

Pursuing CFA Level I, I stay attuned to market dynamics, from cross-border economic trends to investment critiques, ensuring my analytics align with strategic financial imperatives.

Technical Proficiency

Proven in end-to-end workflows: from data ingestion and optimization to deployment and visualization.

Python SQL Power BI TensorFlow Git Databricks

Key projects

Curated implementations showcasing finance-applied analytics:

  • Deep Stock: FOREX Forecasting
    Engineered an LSTM neural network to predict currency fluctuations using normalized time-series data, processing volatile market inputs for actionable trade signals.

    Technologies: TensorFlow, Pandas, Scikit-Learn.

  • Loan Risk Model: Credit Decision Support
    Developed decision trees and logistic regression models to evaluate default probabilities from historical lending data, enhancing approval accuracy in simulated banking scenarios.

    Technologies: TensorFlow, Pandas, Scikit-Learn, YDF.

  • Digit OCR: High-Accuracy Recognition
    Trained a convolutional neural network achieving 98% accuracy on digit extraction documents, streamlining automated data entry.

    Technologies: TensorFlow, OpenCV, NumPy.

  • Finance Math Library: Economic Toolkit
    Created a modular Python library for core financial computations, including discounted cash flows and portfolio optimization, to accelerate quantitative analysis.

    Technologies: NumPy, Pandas.

Explore the full ML suite: oliszymanski/ml-models.

Let's connect. Eager to discuss finance analytics opportunities or collaborations.

Oliwier's GitHub Stats
Advancing data's transformative role in finance—one insight at a time.

Pinned Loading

  1. loan-risk-prediction loan-risk-prediction Public

    a predictive model for assessing loan risk using machine learning techniques and data analysis

    Jupyter Notebook

  2. ml-models ml-models Public

    Notebooks of all ML models that I built.

    Jupyter Notebook

  3. finance-math finance-math Public

    Library with mathematics used spicifically for finance

    Python

  4. genetic-diversity-in-legionella genetic-diversity-in-legionella Public

    Compairing genomes of different strains of legionella to count the diversity of this bacteria

    Python

  5. Count-DNA-Nucleotides Count-DNA-Nucleotides Public

    Used for counting the number of all four nucleotides and finding the percentage number of each of them

    Python

  6. covid-19-data covid-19-data Public

    Python