Sandra O.

Key Skills

Software

Other

Top Subject Matter

Customer and financial data quality assurance for AI/data training readiness

Customer feedback and trend analysis for evaluation-driven improvement of AI-like outputs

Top Data Types

Text

Top Task Types

Data Collection

Freelancer Overview

Data Verification & Cleanup / Quality Control (AI-relevant data preparation). Core strengths include Other. Education includes Bachelor of Economics, Technical University (2016) and Advanced Excel and Statistical Packages, Technical University (2016). AI-training focus includes data types such as Text and labeling workflows including Data Collection, Evaluation, and Rating.

Labeling Experience

Data Verification & Cleanup / Quality Control (AI-relevant data preparation)

OtherTextData Collection

Conducted verification and cleanup of customer and financial records to ensure accuracy prior to downstream processing, analogous to preparing clean structured datasets for AI training. Compiled data into clear formats for reporting and decision-making while maintaining data consistency. Performed quality control by monitoring entries and billing/service records to identify and correct errors. • Verified and cleaned large datasets for accuracy and consistency. • Documented and adhered to operational quality standards during review. • Corrected data integrity issues found in system entries and records. • Translated data findings into actionable insights for improvement workflows.

2013 - 2016

Pattern Identification & Feedback / Iterative Improvement

OtherText

Analyzed customer feedback and market trends to identify gaps and opportunities in information, similar to evaluating model behavior and flagging areas needing refinement. Provided structured feedback on system or process outputs and supported iterative improvement cycles. Regularly reviewed service delivery standards and implemented corrective measures to maintain high accuracy levels. • Identified patterns, anomalies, and edge cases in feedback and trends. • Used data-driven observations to inform strategy and improvement actions. • Reviewed standards and implemented corrective measures to reduce errors. • Contributed feedback to support continuous improvement of outputs.

2016

Education

Work History