I'm a Bioinformatics student at Jagiellonian University with a strong interest in **DevOps, infrastructure automation, Data Science and Software Engineering **. I enjoy building tools that automate developer workflows, managing infrastructure hands-on, and extracting insights from data — from CI/CD pipelines and self-hosted home lab environments to ML models and data analysis pipelines.

🎯 Currently seeking internships in: DevOps / Infrastructure Automation, Software Engineering, Data Science 📍 Based in Poland | 🌍 Open to remote & hybrid opportunities 🔗 LinkedIn · 📧 aleksandra.dziurok@gmail.com

• 🏗️scaffy – DevOps CLI tool automating Python project setup: project structure, Dockerfile, GitHub Actions CI/CD pipeline, and GitHub push — all in one command (Python, subprocess, Docker, GitHub Actions)
• 🎵 Spotify Music Clustering – Unsupervised ML project grouping ~100k tracks into clusters using K-Means on audio features (Python, scikit-learn, Plotly)
• 🖥️ Homelab – Proxmox + TrueNAS – Self-hosted virtualization and secure storage with ZFS and Syncthing (Proxmox, TrueNAS, networking)
• 🌳 KD-Tree Implementation – Custom C++ nearest-neighbor search with benchmarking vs naive approach (C++17, STL, tests)

• Languages: C++ (OOP, STL, algorithms), Python (OOP, scripting, GUI)
• Tools: Git, Linux, CMake, GTest, Jupyter Notebooks
• Sample projects: KD-tree, Task Manager, Rudiments Buddy

• Tools: Docker, GitHub Actions, Git, Bash/shell scripting, Linux
• Infrastructure: Proxmox VE, TrueNAS, ZFS, Syncthing, VM management
• Practices: CI/CD pipelines, containerization, infrastructure automation
• Sample project: scaffy

• Tools: Python (pandas, NumPy, scikit-learn, seaborn, matplotlib, Plotly), SQL (T-SQL)
• Experience:
  • Unsupervised learning — K-Means clustering, Elbow Method, Silhouette Score
  • Regression models (Lasso, Ridge) and model evaluation
  • Data preprocessing, feature engineering, visualization
• Sample projects: Spotify Clustering, Opossum Age Prediction, Emergency Station DB

Some highlights from my portfolio:

1. Spotify Music Clustering – K-Means clustering on ~100k Spotify tracks; discovered acoustic vs energetic music groups using audio features
2. scaffy – DevOps CLI tool that automates Python project scaffolding, Docker setup, GitHub Actions CI/CD, and GitHub repo creation
3. KD-Tree Nearest Neighbor – C++ implementation of a KD-tree with nearest-neighbor search and benchmarks
4. Rudiments Buddy – Python app analysing percussion timing precision against a metronome recording
5. Emergency Station Database – SQL Server + Python GUI + Faker-generated data, mimicking an emergency station system
6. Opossum Age Prediction – Linear regression with Lasso/Ridge regularization on biological data
7. Task Manager (Python) – Terminal-based task manager with categories, file persistence, and exception handling
8. Homelab – Proxmox + TrueNAS – Self-hosted virtualization and secure storage with ZFS and Syncthing

• Data Governance / Data Science Intern – Power BI dashboards, Python/Groovy scripting, SQL data analysis and validation
• Robotics & Programming Instructor – Teaching programming and problem-solving to kids using LEGO Mindstorms/SPIKE Prime
• CAMDA 2025 Participant – Predictive models for antimicrobial resistance in bacteria (team project, Python ML stack)

I'm actively looking for Summer 2026 internships in:

• DevOps / Infrastructure Automation
• Data Science / Machine Learning
• Software Engineering

📫 Email: aleksandra.dziurok@gmail.com
🔗 LinkedIn: linkedin.com/in/aleksandra-janik25

• 🥁 Learning percussion (currently working on Linkin Park covers—still mastering Last Christmas!)
• 🧱 Designing and 3D-printing functional everyday objects (organizers, holders, custom parts)
• 🇯🇵 Studying Japanese (goal: casual conversation, not just "I can sell you a hat")
• 🏕️ Scout leader organising international camps and working on a foundation for a mountain cabin