Carlos D.

Key Skills

Software

No software listed

Top Subject Matter

Smart grid telemetry

sensor/IoT data

power electronics

Top Data Types

3D Sensor

Computer Code Programming

Text

Document

Top Task Types

Classification

Freelancer Overview

Senior Data & Systems Engineer / Senior Electrical Engineer — NextGen Power Solutions (Data Annotation & Curation, taxon. Brings 9+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Python, MATLAB, and Simulink. Education includes Master of Science in Electrical Engineering, University of California, San Diego (UCSD) (2021) and Bachelor of Science in Electrical Engineering, University of California, Los Angeles (UCLA) (2018). AI-training focus includes data types such as 3D Sensor and labeling workflows including Classification

Labeling Experience

Senior Data & Systems Engineer / Senior Electrical Engineer — NextGen Power Solutions (Data Annotation & Curation, taxonomy, automated validation)

3D SensorClassification

Led curation and precise labeling of massive time-series telemetry and sensor datasets to create high-fidelity ground truth for ML systems. Developed standardized labeling schemas, taxonomies, and edge-case handling guidelines for power-electronic behavior in smart-grid scenarios. Built automated validation protocols to improve dataset cleanup and labeling efficiency while maintaining high data fidelity. • Labeled grid-tied inverter and energy storage telemetry from distributed energy resource systems. • Created and maintained annotation taxonomies and labeling guidelines for predictive control. • Conducted compliance-oriented QA audits against IEEE 1547 and IEC 61850 frameworks. • Coordinated cross-functional teams to align on annotation standards and data-quality requirements.

2021 - Present

Data Systems & Engineering Associate / Electrical Engineering Associate — Pacific Tech Engineering Group (Sensor data labeling, anomaly tagging, metadata integration)

3D SensorClassification

Captured, cleaned, and labeled complex high-frequency sensor signal data (voltages, currents, waveforms) to support predictive maintenance classification models. Established systematic anomaly tagging methods to categorize signal sags, swells, and transients into structured labels for downstream classification. Authored annotation documentation and developed metadata integration scripts to improve parsing, structural labeling, and reduce human error. • Performed sensor data labeling for predictive maintenance model inputs using MATLAB and LabVIEW. • Labeled anomalies and maintained structured datasets for classification. • Implemented automated raw data parsing and metadata tagging for consistent labeling. • Produced annotation guides and documentation to make labeling processes reproducible for compliance.

2018 - 2021

Education

Work History