Narcis P.

Key Skills

Software

No software listed

Top Subject Matter

E-commerce/catalog compliance auditing for ML training

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Top Data Types

Text

Document

Top Task Types

Classification

Freelancer Overview

Compliance Associate (Data Labeling and Annotation for AI/ML). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Master’s Degree in Engineering, Gheorghe Asachi Technical University of Iași (2025) and Bachelor’s Degree in Engineering, Gheorghe Asachi Technical University of Iași (2023). AI-training focus includes data types such as Text and labeling workflows including Classification.

Labeling Experience

Compliance Associate - Amazon

Classification

As a Compliance Associate, you managed data classification and structural labeling processes to support training and continuous improvement of machine learning models. You ensured data accuracy by performing systematic verification and analysis using Excel and internal auditing platforms. You evaluated data against internal policy criteria and contributed to monitoring automated decisions through human-in-the-loop compliance checks. • Managed data compliance validation and anomaly identification for large-scale catalogs. • Performed data verification and analysis using advanced Excel functions and internal tools. • Applied internal policies and guidelines to assess data compliance. • Reported discrepancies to improve the efficiency and accuracy of ML algorithms.

2023 - Present

Compliance Associate (Data Labeling and Annotation for AI/ML)

TextClassification

Managed data classification and structural labeling workflows for large-scale catalogs to train and improve machine learning models. Performed data verification and analysis to ensure compliance with auditing criteria and internal policies, including human-in-the-loop validation of automated decisions. Identified anomalies and data discrepancies and reported findings to improve algorithm efficiency and accuracy. • Applied internal policy guidelines and compliance criteria to evaluate labeled data • Verified catalog data using advanced Excel functions and internal compliance auditing web platforms • Supported human-in-the-loop review of automated decision outputs • Tracked and escalated data issues to maintain labeling quality

2023 - Present

Education

Work History