Andreas C.

Key Skills

Software

Label Studio

Top Subject Matter

Earth observation and illegal dumping detection (geospatial ML)

Information security risk assessment and SOC monitoring

Top Data Types

Text

Top Task Types

Classification

Entity (NER) Classification

Freelancer Overview

Earth Observation position (ML-based illegal dumping detection using Label Studio) – PERIOPSIS. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Label Studio and Microsoft Sentinel. Education includes Bachelor of Science, University of Cyprus (2022) and Lyceum School Diploma, Lyceum School (2021). AI-training focus includes data types such as Geospatial, Tiled Imagery, and Text and labeling workflows including Classification, Evaluation, and Rating.

Labeling Experience

Information Security Intern – Bank of Cyprus

Text

Performed information security monitoring and analysis workflows that involved evaluating and assessing third-party and SaaS security risks. While not explicitly described as data annotation, the role included structured risk assessment activities that required labeling/triaging security-relevant findings for operational use. Applied monitoring queries and security telemetry to support risk evaluation and incident-oriented analysis. • Conducted third-party and SaaS risk assessments for structured evaluation • Performed shadow IT analysis to triage security-relevant assets • Supported SOC monitoring via query-driven investigation (KQL) • Used Sentinel/SOC tooling and related telemetry to evaluate findings

2025 - 2025

Earth Observation and Geospatial ML Project Assistant - Periopsis

Label StudioEntity (NER) Classification

Worked on machine-learning based illegal dumping detection using specialized labeling tooling while focusing on geospatial analysis and results presentation. Applied Python and QGIS to analyze geographic data and visualize findings in a user-friendly interface. Supported development efforts by combining ML workflows with geospatial analytics and debugging practices. • Developed ML-driven illegal dumping detection workflows • Performed geospatial analysis with Python and QGIS • Built results visualization using Python GUI • Collaborated on end-to-end analytics and troubleshooting activities

2024 - 2024

Earth Observation position (ML-based illegal dumping detection using Label Studio) – PERIOPSIS

Label StudioClassification

Worked on ML-based illegal dumping detection by preparing and labeling geospatial observations to support model training. Used Label Studio to create annotation work for the detection pipeline and ensure labeled outputs were consistent with the task requirements. The labeled data was then leveraged for analysis and downstream visualization of results. • Created and managed labeled examples for illegal dumping detection • Reviewed annotation quality and label consistency in the dataset • Supported training-ready dataset preparation for ML workflows • Coordinated labeled geospatial data with visualization outputs

2024 - 2024

Education

Work History