Stanley Munyambu

Key Skills

Software

Clickworker

CloudFactory

CVAT

Mercor

Mindrift

OneForma

Remotasks

Roboflow

Scale AI

Toloka

Other

Top Subject Matter

No subject matter listed

Top Data Types

Document

Image

Text

Top Label Types

Action Recognition

Bounding Box

Data Collection

Polygon

Text Generation

Freelancer Overview

I am a detail-oriented professional in data management for AI training purposes, having more than five years of experience in data validation, data management, and data workflow in various industries. My experience has involved hands-on data collection, data verification, data annotation, and data quality checks for various purposes of artificial intelligence applications. I worked for purposes of training artificial intelligence applications in video labeling using Atlas Capture, data annotation using Remotask, and data processing at Impact Outsourcing, where data precision, confidentiality, and consistency mattered. In addition to AI-related projects, I have finished data acquisition and lead generation projects for Upwork, as well as online research for Fiverr, providing organized and clean data within set criteria and tight timelines. I have extensive attention to detail, rapid learning, and experience with spreadsheet, database, and report tools, backed up by formal education for data management and ongoing learning for data analysis and AI automation. This skill set ensures I can contribute towards AI training and labeling projects, which require precision, scalability, and quality assurance.

Labeling Experience

Lead Generation & Contact Verification Specialist

OtherTextData Collection

Manually verified and marked up key contact information such as name, title, phone number, email, and website for over 50 private K-12 schools in the U.S. via open-source research tools such as official school websites, LinkedIn, and educational directories. As part of the process, I double-checked information for validity, filled in missing information, and coded valid information using a system of colored status flags in an Excel document. This task requires the same level of scrutiny, deduction where information was missing, as well as a focus on accuracy, as the process of validating attributes at OpenTrain. Much like at OpenTrain, this was not an automated process, instead relying on human research.

Manually verified and marked up key contact information such as name, title, phone number, email, and website for over 50 private K-12 schools in the U.S. via open-source research tools such as official school websites, LinkedIn, and educational directories. As part of the process, I double-checked information for validity, filled in missing information, and coded valid information using a system of colored status flags in an Excel document. This task requires the same level of scrutiny, deduction where information was missing, as well as a focus on accuracy, as the process of validating attributes at OpenTrain. Much like at OpenTrain, this was not an automated process, instead relying on human research.

2023 - 2024

Industrial Object Detection and Image Annotation for Computer Vision

Scale AIImageBounding Box

In this project, I specialized in annotating images of industrial environment scenes by marking objects like grids, sumps, machines, and structural components with bounding boxes. I annotated these images to train object detection models. In these annotations, emphasis was put on accuracy, consistency, and proper object categorization to ensure high-quality annotated data, which is useful in real-world applications of artificial intelligence.

In this project, I specialized in annotating images of industrial environment scenes by marking objects like grids, sumps, machines, and structural components with bounding boxes. I annotated these images to train object detection models. In these annotations, emphasis was put on accuracy, consistency, and proper object categorization to ensure high-quality annotated data, which is useful in real-world applications of artificial intelligence.

2020 - 2023

Medical Ultrasound Image Annotation for AI Training

CVATMedical DicomPolygon

This project involved annotating medical ultrasound images for AI training purposes. Key anatomical structures, including vessel walls and collateral regions, were carefully identified and labeled to support supervised learning models used in medical diagnostics and clinical decision support. The work required high attention to detail, accurate boundary marking, and adherence to strict labeling guidelines to ensure the data met healthcare quality and consistency standards.

This project involved annotating medical ultrasound images for AI training purposes. Key anatomical structures, including vessel walls and collateral regions, were carefully identified and labeled to support supervised learning models used in medical diagnostics and clinical decision support. The work required high attention to detail, accurate boundary marking, and adherence to strict labeling guidelines to ensure the data met healthcare quality and consistency standards.

2021 - 2022

Education

Work History