Data science and machine learning in Warsaw

Igor Kołodziej

Data Scientist & ML Engineer

I'm a data scientist and ML engineer based in Warsaw. I mainly work on causal inference, NLP, and computer vision. I like projects where the evaluation is as important as the model and where the result can be inspected and rerun.

My name is sometimes written without Polish characters as Igor Kolodziej.

Portrait of Igor Kołodziej Warsaw, Poland

Selected projects

A few projects I have worked on.

They cover my master's research, API testing, computer vision, text analysis, and biosignals.

02

Agents | APIs | Evaluation

Agentic API readiness

A Python tool for checking how an OpenAPI specification behaves when an agent uses the API through MCP. It validates the spec, runs test workflows, grades traces, and removes sensitive values from reports.

  • Python
  • OpenAPI
  • MCP
  • CI
03

Computer vision | Remote sensing

EuroSAT RGB classification

A ResNet18 classifier for ten EuroSAT land-use classes, with a fixed data split and three preprocessing variants.

89.53% previously reported test accuracy

04

NLP | Streaming data

Reddit supplement NLP

A streaming text-analysis tool for finding supplement mentions, questions, sentiment, negation, and aspects in Reddit discussions.

  • spaCy
  • Transformers
  • VADER
05

Biosignals | R/Shiny | ML

StressAware HRV

A course project with Paula Banach that combines an R/Shiny app with Python HRV preprocessing and WESAD-based stress experiments.

Educational research software. It is not a medical or diagnostic tool.

Background

Education and technical background.

2026

MSc, Data Science & Business Analysis

University of Warsaw, graduated with distinction

BSc

Neuroinformatics

Biosignals, statistics, and computational neuroscience

Focus

What I work with

Python, R, SQL, PyTorch, scikit-learn, XGBoost, Transformers, EconML, Docker, and GitHub Actions

Contact

Want to get in touch?

LinkedIn is the easiest way to reach me. The source code for the projects above is available on GitHub.