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Achievement: Pull Sharkx2

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Achievement: Pull Sharkx2

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gaswiz/README.md
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Professional Snapshot

Support Engineer at Pandectes with a BSc in Computer Science (First Class Honours) from the University of East London. I combine production support discipline with software engineering execution and data-driven decision making.

My strongest technical focus areas are Machine Learning, Algorithms, Predictive Analytics, and Microsoft Azure.

Current Technical Direction

  • Production-grade ML workflows and evaluation
  • Algorithmic optimization and complexity-aware implementations
  • Forecasting systems for predictive business insights
  • Cloud-oriented engineering and deployment on Azure

Core Strengths

ML Algorithms Predictive Analytics Azure

Domain Capability Practical Value
Machine Learning Model training, tuning, validation pipelines Reliable predictive performance in real scenarios
Algorithms Optimization, efficiency analysis, scalable logic Faster and more robust software systems
Predictive Analytics Forecasting, trend modeling, data interpretation Better product and business decisions
Cloud (Azure) Cloud-first architecture and deployment mindset Operationally stable and maintainable services

Languages & Technology Stack

Tech Icons

Microsoft Azure Focus

Microsoft Azure Azure AI Azure Data Azure DevOps

Expand Full Focus Areas

Machine Learning

  • Supervised/unsupervised workflows
  • Feature engineering
  • Metric-driven model iteration

Algorithms

  • Time and space complexity focus
  • Problem decomposition
  • Performance optimization

Predictive Analytics

  • Forecasting and trend analysis
  • Decision support models
  • Data-to-insight communication

Featured Work

P.Y.T.H.I.A.

A predictive analytics platform for marketing performance optimization using AI/ML methods. Built to improve forecast quality, strategy planning, and measurable decision outcomes.

Type Focus Goal

2026 Engineering Goals

  1. Deploy more production-ready ML systems with clear success metrics.
  2. Deepen advanced algorithmic design for high-scale problem solving.
  3. Expand Azure architecture and operational deployment capability.
  4. Build predictive tools that convert data directly into decisions.

Contact

Email: konstantinospanagiotaropoulos@gmail.com
LinkedIn: Konstantinos Panagiotaropoulos

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  1. pythia-core pythia-core Public

    Machine learning system for predicting digital advertising campaign performance using regression models and a Flask API.

    Jupyter Notebook 1

  2. csc-2026 csc-2026 Public

    This repository showcases the CSC Greece 2025 CTF challenges I solved as part of a team, highlighting my contributions, thought process, technical writeups, and hands-on cybersecurity methodology.