Samuel Akande

Key Skills

Software

Label Studio

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Classification

Freelancer Overview

I am a data professional with hands-on experience in data cleaning, preparation, and exploratory analysis for diverse real-world projects. My background includes working with large, complex datasets, ensuring data accuracy and reliability through careful data collection and annotation processes. I have applied statistical methods and used Python, R, SQL, and tools like Power BI, Tableau, and Excel to deliver actionable insights and build predictive models. With a strong track record of delivering high-quality, client-focused solutions and developing detailed reports and visualizations, I am skilled at transforming raw data into reliable training datasets for AI and machine learning applications. My attention to detail, problem-solving abilities, and commitment to data quality make me well-suited for roles focused on data labeling and AI training data.

Labeling Experience

Data Annotation & Quality Analyst (Freelance/Contract, Remote)

Label StudioTextClassification

As a Data Annotation & Quality Analyst, I annotated structured and unstructured text datasets for machine learning training pipelines. I ensured all labels complied with guidelines and maintained labeling accuracy above 98%. I also collaborated with ML teams to improve taxonomy and flagged edge cases for better model robustness. • Labeled text data for classification, sentiment analysis, and entity recognition • Conducted regular quality assurance reviews and resolved inconsistencies • Used Python to analyze dataset distributions and detect anomalies • Validated annotation consistency and improved dataset clarity with team input

As a Data Annotation & Quality Analyst, I annotated structured and unstructured text datasets for machine learning training pipelines. I ensured all labels complied with guidelines and maintained labeling accuracy above 98%. I also collaborated with ML teams to improve taxonomy and flagged edge cases for better model robustness. • Labeled text data for classification, sentiment analysis, and entity recognition • Conducted regular quality assurance reviews and resolved inconsistencies • Used Python to analyze dataset distributions and detect anomalies • Validated annotation consistency and improved dataset clarity with team input

2024

BI Analyst (ML Dataset Support)

Label StudioTextClassification

In my role as BI Analyst (ML Dataset Support), I cleaned and prepared large text datasets and developed validation scripts for labeled outputs. I evaluated annotation quality and identified labeling biases through exploratory data analysis. I summarized overall dataset quality and annotation consistency in detailed reports. • Evaluated model performance metrics using precision, recall, F1-score, and accuracy • Developed and implemented Python scripts to cross-check labeled outputs • Performed exploratory data analysis to support robust labeling • Created comprehensive reports on dataset and annotation quality

In my role as BI Analyst (ML Dataset Support), I cleaned and prepared large text datasets and developed validation scripts for labeled outputs. I evaluated annotation quality and identified labeling biases through exploratory data analysis. I summarized overall dataset quality and annotation consistency in detailed reports. • Evaluated model performance metrics using precision, recall, F1-score, and accuracy • Developed and implemented Python scripts to cross-check labeled outputs • Performed exploratory data analysis to support robust labeling • Created comprehensive reports on dataset and annotation quality

2022 - 2025

Project: Sentiment Analysis Annotation Project

Label StudioTextClassification

During the Sentiment Analysis Annotation Project, I created a structured labeling framework for sentiment data and documented clear annotation guidelines. I measured inter-annotator agreement to ensure consistency and applied quality assurance methodologies. This project contributed to substantial improvements in training data for AI models. • Developed framework and guidelines for sentiment data annotation • Labeled and reviewed sentiment analysis data to ensure high quality • Assessed consistency via inter-annotator agreement metrics • Implemented quality assurance strategies for reliable results

During the Sentiment Analysis Annotation Project, I created a structured labeling framework for sentiment data and documented clear annotation guidelines. I measured inter-annotator agreement to ensure consistency and applied quality assurance methodologies. This project contributed to substantial improvements in training data for AI models. • Developed framework and guidelines for sentiment data annotation • Labeled and reviewed sentiment analysis data to ensure high quality • Assessed consistency via inter-annotator agreement metrics • Implemented quality assurance strategies for reliable results

2023 - 2023

Project: Text Classification Dataset Validation

Label StudioTextClassification

For the Text Classification Dataset Validation project, I cleaned, labeled, and systematically reviewed more than 50,000 text samples. I built custom Python scripts to validate consistency and reduce mislabel rate. My work resulted in a significant improvement in overall dataset reliability for downstream AI model development. • Labeled and validated high-volume text classification datasets • Detected and corrected labeling inconsistencies with Python tools • Reduced mislabel rate by 15% through direct, methodical review • Ensured adherence to annotation guidelines throughout the project

For the Text Classification Dataset Validation project, I cleaned, labeled, and systematically reviewed more than 50,000 text samples. I built custom Python scripts to validate consistency and reduce mislabel rate. My work resulted in a significant improvement in overall dataset reliability for downstream AI model development. • Labeled and validated high-volume text classification datasets • Detected and corrected labeling inconsistencies with Python tools • Reduced mislabel rate by 15% through direct, methodical review • Ensured adherence to annotation guidelines throughout the project

2023 - 2023

Education

Work History