Michael O.

Key Skills

Software

Labelbox

CVAT

Top Subject Matter

AI/ML model training and dataset quality for multimodal content.

Epidemiological modeling and statistical analysis for public health research.

Software quality assurance and solution verification for evaluation-style validation.

Top Data Types

Image

Top Task Types

Classification

Freelancer Overview

Data Annotator & AI Model Trainer at Sigma. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, CVAT, and N. Education includes Bachelor of Science, Belhaven University (2024) and Certificate, Sigma Academy (Online) (2023). AI-training focus includes data types such as Image, Medical, and DICOM and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Freelance Software Tester at UTest

Created structured, verifiable software test cases across concurrent projects to validate outputs against defined correct answers. Verified software behavior and documented findings in written reports, mirroring the solution verification mindset used in AI evaluation tasks. Produced precise, technical documentation supporting reproducibility and quality standards. • Designed rigorous test statements to enable clear correctness checks. • Conducted output verification against expected results for each test case. • Maintained detailed written reporting of issues and resolutions. • Ensured high standards of technical writing and precision throughout testing.

2024 - 2024

Data Analyst at Center for Disease Modeling and Analysis

Classification

Performed data-driven analysis that supported disease modeling work using epidemiological datasets and structured analytical workflows. Used Python and SQL to manipulate, query, and visualize large health datasets to produce insights for research decision-making. Designed reproducible analytical methodologies documented for cross-team collaboration and applicability to computationally intensive problem design. • Applied statistical modeling techniques aligned with real research challenges. • Focused on epidemiological data analysis and modeling support rather than traditional labeling. • Produced analysis outputs that informed downstream computational task creation. • Ensured documentation clarity and reproducibility for collaborative work.

2023 - 2024

Data Annotator & AI Model Trainer at Sigma

LabelboxImage

Developed and validated structured AI/ML problem sets used for model training and evaluation, ensuring tasks required multi-step reasoning. Created annotation guidelines and quality validation frameworks to support accurate labeling outcomes and dataset integrity. Analyzed annotation outputs with Python to identify quality gaps and drive iterative improvements to the labeled datasets. • Managed large-scale multimodal datasets including image, text, audio, and video. • Documented problem statements and verified correct outputs for each evaluation task. • Collaborated with data scientists and engineers to maintain rigorous accuracy standards. • Optimized labeling workflows for efficiency and consistency using annotation platforms.

2023 - 2024

Education

Work History