Favour A.

Key Skills

Software

Label Studio

Argilla

Encord

Top Subject Matter

IT, Software Engineering & Tech Platforms

Banking, Financial Services & Insurance

Retail, E-Commerce & Supply Chain Logistics

Top Data Types

Text

Top Task Types

Question Answering

Text Summarization

RLHF

Data Collection

Classification

Segmentation

Freelancer Overview

As a Data Analyst with a strong foundation in machine learning, statistical modeling, and data preprocessing, I bring a rigorous and detail-oriented approach to AI training and data labeling. My experience revolves around managing large-scale datasets, where I have been responsible for cleaning, transforming, and interpreting complex metrics to ensure high data integrity. I am highly adept at utilizing tools like SQL, R, and Excel to restructure information, which directly translates to the creation of precise, high-quality ground-truth datasets required for robust model training. What sets me apart is the unique combination of my analytical background and hands-on engagement with AI tools and predictive modeling. I excel at data cleaning, variance analysis, and iterative validation, ensuring that annotations and training prompts are accurate, consistent, and free of bias. Comfortable with both the technical execution of data modeling and the nuanced evaluation of AI outputs, I am well-equipped to contribute to high-level model fine-tuning, Reinforcement Learning from Human Feedback (RLHF), and complex data annotation workflows.

Labeling Experience

RLHF (Reinforcement Learning from Human Feedback) for Advanced Data Analytics & Code Generation

TextText Summarization

Served as a domain-expert annotator to train a specialized Large Language Model (LLM) capable of generating advanced data analytics workflows and automation scripts. The role involved creating complex technical prompts, evaluating model-generated code (SQL, R, VBA, and Excel formulas) for functional accuracy, and ranking outputs based on safety, efficiency, and clarity. This high-quality human feedback directly improved the model's ability to interpret analytical requests and deliver bug-free, deployment-ready code.

2025 - 2026

Education

Work History